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Blinding in clinical...

Blinding in clinical trials and other studies

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Peer review
Simon J Day a , manager, clinical biometrics ,
Douglas G Altman b , professor of statistics in medicine
a Leo Pharmaceuticals, Princes Risborough, Buckinghamshire HP27 9RR
b ICRF Medical Statistics Group, Institute of Health Sciences, Oxford OX3 7LF
Correspondence to: S J Day

Human behaviour is influenced by what we know or believe. In research there is a particular risk of expectation influencing findings, most obviously when there is some subjectivity in assessment, leading to biased results. Blinding (sometimes called masking) is used to try to eliminate such bias.

It is a tenet of randomised controlled trials that the treatment allocation for each patient is not revealed until the patient has irrevocably been entered into the trial, to avoid selection bias. This sort of blinding, better referred to as allocation concealment, will be discussed in a future statistics note. In controlled trials the term blinding, and in particular “double blind,” usually refers to keeping study participants, those involved with their management, and those collecting and analysing clinical data unaware of the assigned treatment, so that they should not be influenced by that knowledge.

The relevance of blinding will vary according to circumstances. Blinding patients to the treatment they have received in a controlled trial is particularly important when the response criteria are subjective, such as alleviation of pain, but less important for objective criteria, such as death. Similarly, medical staff caring for patients in a randomised trial should be blinded to treatment allocation to minimise possible bias in patient management and in assessing disease status. For example, the decision to withdraw a patient from a study or to adjust the dose of medication could easily be influenced by knowledge of which treatment group the patient has been assigned to.

In a double blind trial neither the patient nor the caregivers are aware of the treatment assignment. Blinding means more than just keeping the name of the treatment hidden. Patients may well see the treatment being given to patients in the other treatment group(s), and the appearance of the drug used in the study could give a clue to its identity. Differences in taste, smell, or mode of delivery may also influence efficacy, so these aspects should be identical for each treatment group. Even colour of medication has been shown to influence efficacy. 1

In studies comparing two active compounds, blinding is possible using the “double dummy” method. For example, if we want to compare two medicines, one presented as green tablets and one as pink capsules, we could also supply green placebo tablets and pink placebo capsules so that both groups of patients would take one green tablet and one pink capsule.

Blinding is certainly not always easy or possible. Single blind trials (where either only the investigator or only the patient is blind to the allocation) are sometimes unavoidable, as are open (non-blind) trials. In trials of different styles of patient management, surgical procedures, or alternative therapies, full blinding is often impossible.

In a double blind trial it is implicit that the assessment of patient outcome is done in ignorance of the treatment received. Such blind assessment of outcome can often also be achieved in trials which are open (non-blinded). For example, lesions can be photographed before and after treatment and assessed by someone not involved in running the trial. Indeed, blind assessment of outcome may be more important than blinding the administration of the treatment, especially when the outcome measure involves subjectivity. Despite the best intentions, some treatments have unintended effects that are so specific that their occurrence will inevitably identify the treatment received to both the patient and the medical staff. Blind assessment of outcome is especially useful when this is a risk.

In epidemiological studies it is preferable that the identification of “cases” as opposed to “controls” be kept secret while researchers are determining each subject's exposure to potential risk factors. In many such studies blinding is impossible because exposure can be discovered only by interviewing the study participants, who obviously know whether or not they are a case. The risk of differential recall of important disease related events between cases and controls must then be recognised and if possible investigated. 2 As a minimum the sensitivity of the results to differential recall should be considered. Blinded assessment of patient outcome may also be valuable in other epidemiological studies, such as cohort studies.

Blinding is important in other types of research too. For example, in studies to evaluate the performance of a diagnostic test those performing the test must be unaware of the true diagnosis. In studies to evaluate the reproducibility of a measurement technique the observers must be unaware of their previous measurement(s) on the same individual.

We have emphasised the risks of bias if adequate blinding is not used. This may seem to be challenging the integrity of researchers and patients, but bias associated with knowing the treatment is often subconscious. On average, randomised trials that have not used appropriate levels of blinding show larger treatment effects than blinded studies. 3 Similarly, diagnostic test performance is overestimated when the reference test is interpreted with knowledge of the test result. 4 Blinding makes it difficult to bias results intentionally or unintentionally and so helps ensure the credibility of study conclusions.

De Craen AJM ,
de Vries AL ,
Schulz KF ,
Chalmers I ,
Lijmer JG ,
Heisterkamp S ,
Bonsel GJ ,
van der Meulen JH

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Blinding: A detailed guide for students

Posted on 26th June 2017 by Saul Crandon

What is blinding?

Blinding is about ensuring that participants and/or personnel within a study are unaware of a particular element of that study. It is done to minimise bias [1,2]. Although blinding can be implemented in a range of study designs, for the purposes of this article, we will focus specifically on randomised controlled trials (RCTs). You will often see that RCTs are described as ‘double-blinded’. This means that both the study participants and researchers are unaware of the group allocations. In other words, neither the patients nor researchers know who is receiving the ‘actual’ intervention or the ‘dummy’ intervention.

There are a number of different levels of blinding:

Single = One party is blinded to treatment allocation, this usually refers to either patients only or researchers only

Double = Two parties are blinded to treatment allocation, this usually refers to patients and researchers

Triple = Three parties are blinded to treatment allocation, this usually includes patients, researchers and then other staff involved in the running of the study (e.g. data collectors, statisticians etc).

Common methods of blinding

One of the most common methods of blinding in RCTs is the use of seemingly identical medications; one ‘active’ pill and one ‘placebo’ pill. As they are physically identical, it is impossible for patients and researchers to discern which pill is the active one based on appearance alone . This is an example of robust blinding. If it is not robust, there is a risk of un-blinding. This is demonstrated well by a study of lavender and its relation to falls in the elderly [3] . Those receiving the lavender had a patch with the scent whereas controls were given a placebo patch. However, there is a risk that the patients themselves and their caregivers would also detect the lavender scent of those receiving the active intervention. This shatters the blinding process and hence, bias is then introduced either consciously or subconsciously.

Reports have found that less than 25% of RCTs adequately report blinding [4] . Moreover, the terms used for blinding have many different interpretations and are not universally defined [5] . Therefore, the CONSORT guidelines for the optimal reporting of RCTs recommends that blinding should not only be robust, but that the manuscript must explicitly state the method of blinding used as well as which parties in the trial were blinded [6,7] . It is not enough to simply state the trial is ‘double-blinded’ without further elaboration.

It is notoriously difficult to use blinding in surgical RCTs. This is because the patient is likely to know whether they have been cut open or not. To avoid this, often the tested intervention is an internal surgical technique or the wound is covered using the same large dressing and hence the patients are unaware whether or not they have received an active intervention.

However, it can be difficult to blind the surgeon to the tested intervention as they must perform the procedure. This still remains a challenge in medical research. (Although it is not impossible to blind surgeons: have a look at this for more information). It is recommended that the groups are treated as equally as possible, blinding should be attempted in other areas of the study and that any lack of blinding should be recognised in the limitations section [8]. This allows clinicians and other decision-makers to accurately appraise the study when deciding whether to use its results to inform their medical practice.

Why is blinding important?

Blinding is important for the validity of RCTs. Without it there are number of biases that are unwillingly introduced. It has been shown in an assessment of 33 meta-analyses, encompassing 250 RCTs, that without blinding, odds ratios were exaggerated by up to 17% (P=0.01) . This highlights the importance of not just reading medical literature, but appraising it with a critical lens [9] . This is supported by other analyses of an array of methodological aspects of RCTs [10] .

Knowledge of group allocations may affect behaviour in the trial. For example, researchers may assess patients who are receiving the active intervention more closely than those receiving the control and hence be more likely to pick up on beneficial and/or adverse effects. Similarly, if patients are aware that they are receiving the active intervention they may be more likely to check for side effects and be more likely to drop out.

Furthermore, there is a possibility that if the data collectors or statisticians are aware of treatment allocation then they may also provide a biased assessment of the group outcomes. Even if these people are trustworthy, differences can occur subconsciously, as part of human nature. To eliminate this and ensure independence, it is best that these people are also blinded in an RCT. The trend of going beyond ‘double-blinding’ to ‘triple-blinding’ is becoming more popular, as awareness of the methodological pitfalls in clinical research is improving.

Although more logistically challenging, it is best to blind participants, clinicians/researchers, data collectors and statisticians/data analysts . Ensuring everyone is unaware of treatment allocations minimises bias.

What about when blinding is not possible?

In cases where blinding is not possible or feasible, the outcome measures must be objective! If you are reading a study that is un-blinded, with subjective outcome measures, then you may as well stop reading it and move on. This is because, if a patient is aware they are receiving the active intervention and the outcome measure is subjective, such as ‘how much pain they are experiencing’, their reporting is likely to be biased. Knowledge of the group assignment can consciously or subconsciously cause the patient to feel better and report improved subjective pain tolerance. This is not a reliable study design and the results should not be interpreted with any certainty.

It should be emphasised that the use of objective outcome measures are not a replacement for robust blinding in clinical trials. Blinding should be used wherever possible.

Allocation concealment

Allocation concealment is ensuring that the person(s) randomising participants does not know what the next treatment allocation must be. This is an often underappreciated aspect of many trials that may lead to significant selection bias and invalid conclusions if not implemented [6,7].

CONSORT guidelines recommend that all RCTs have a robust method of randomisation to ensure its validity and minimise bias [6,7] . Even otherwise well-designed studies can be undermined if proper allocation concealment is not performed. If the randomisation sequence is widely available to the researchers then this can influence who is recruited.

For example, if the doctor is in clinic, they may believe a patient may not perform well with the experimental drug. As a result, the clinician may ‘skip’ this patient and not recruit them. Similarly, if the clinician knows a particular patient that they think will be ‘good’ for the experimental drug group, and the allocation sequence is known, they may ‘skip’ the prior patients so that the patient they want is assigned to the experimental drug. These scenarios compromise the randomisation process and introduce bias.

The traditional method of using sealed envelopes for allocation is not robust. It has been documented that researchers have used a bright light to see through these envelopes, or that the envelopes have been opened prior to a clinic starting. Instead, best-practice recommends the use of external randomisation sequencing and delivery of the allocation via telephone. This prevents researchers from potentially influencing the allocations.

Blinding is an important foundation for ensuring internal validity and reducing observer bias
Blinding must be robust and methods of blinding should be reported in detail
Where possible, RCTs should be at least double-blinded, and should have more blinding where possible (this includes: patients, clinicians/researchers, data collectors and statisticians)
Where blinding is not feasible, this should be recognised as a limitation and blinding should be attempted in other areas of the trial
Outcome measures should ideally be objective, particularly when blinding is not possible
Allocation concealment is important to prevent allocation bias
Allocation concealment should ideally be performed using an external, independent telephone system

References (pdf)

Saul Crandon

No Comments on Blinding: A detailed guide for students

This is such a great post

thanks for share your valuable posting.

The article was very interested .

Hello mr crandon. Is it feasible we set up a before- after clinical trial which we blinde the researcher not patient(single blind) and our control consume traditional medication and case get new medication? Will this study valid or we must blind both of patients and researcher? Best regard.

Dear Ali. I’m sorry for the delay in replying to you. One of my colleagues has suggested this article: Schulz, K. F., & Grimes, D. A. (2002). Blinding in randomised trials: hiding who got what. The Lancet, 359(9307), 696–700. doi:10.1016/s0140-6736(02)07816-9 as being a useful guide for you. It is, unfortunately, behind a paywall, but I hope you have access through your institution. I hope this provides some clarity to your situation. Many thanks. Emma.

The article was very easy to read and easily understood. I appreciate that because it makes the learning enjoyable as well. Very interesting article

please very interested in all biostats topics, keep me updated

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What is blinding?

Blinding (or masking) is the process used in experimental research by which study participants, persons caring for the participants, persons providing the intervention, data collectors and data analysts are kept unaware of group assignment (control vs intervention). Blinding aims to reduce the risk of bias that can be caused by an awareness of group assignment. With blinding, outcomes can be attributed to the intervention itself and not influenced by behaviour or assessment of outcomes that can result purely from knowledge of group allocation.

Why incorporate blinding?

Performance bias refers to systematic differences between the treatment and control groups resulting from care that was provided, or exposure to factors other than the interventions of interest. After enrolment into the study, blinding of participants and personnel may reduce the risk that knowledge of which intervention was received affects outcomes. If blinding is not incorporated or is unsuccessful, participants may respond better if they know they have received a promising new treatment. On the other hand, if participants are aware that they are not receiving an active treatment they may be less likely to comply with the study protocol, more likely to seek additional treatment and more likely to leave the study without providing outcome data. 5 The healthcare providers who are blinded to participant allocation are much less likely to transfer their values to participants or to provide differential treatment to the active and placebo groups. 5 However, blinding may not be possible in some studies where the intervention is obvious to the participants and/or persons administering the intervention (eg, an exercise intervention). Such studies can take other measures to reduce the risk of bias, such as treating participants according to a strict protocol to reduce the risk of differential behaviours by persons administering the intervention.

Blinding of outcome assessors is equally important to reduce the introduction of bias into the assessments and should be attempted whenever possible. 5 Outcome assessments may be made by the participants themselves, by their healthcare providers, or by independent assessors. Blinding of the statistical analysts is achievable by simply labelling the participants' data with non-identifying codes. 5

How to implement blinding?

Blinding is not a simple procedure. The researchers often need to engage a variety of approaches to enhance blinding. Boutron et al 6 conducted a systematic review of methods used in pharmacological RCTs to establish blinding of patients and/or healthcare providers. These included providing treatments in identical form, specific methods to mask characteristics of the treatments (eg, added flavour or colour), or use of double dummy procedures and even simulation of an injection.

Methods to avoid unblinding involved use of active placebo, centralised assessment of side effects, and patients informed only in part about the potential side effects of each treatment. Some of the methods used for blinding outcome assessors included centralised assessment of complementary investigations, clinical examination that involved the use of video, audiotape or photography, and adjudication of clinical events. Clearly there are ethical considerations to blinding. All blinding approaches should be explained as part of the method and receive ethical approval from research ethics boards.

How to assess if blinding has been successful?

An attempt to blind participants and personnel does not always ensure successful blinding in practice. For example, for many blinded drug trials, the side effects of the drugs can reveal group allocation, unless the study compares two rather similar interventions (eg, drugs with similar side effects, or uses an active placebo. 6 It has been suggested that it would be useful to ask trial participants at the end of the trial to guess which treatment they have received, 7 , 8 and some reviews of such reports have been published. 7 , 9 Evidence of correct guesses exceeding 50% would suggest that blinding may have been broken. However, responses may simply reflect the patients' experiences in the trial. A good outcome will tend to be more often attributed to an active treatment, and a poor outcome to a placebo. 10

Risk of bias may be high for some outcomes and low for others. For example, knowledge of the assigned intervention may impact on behavioural outcomes (eg, number of visits to their physicians), while not impacting on physiological outcomes or mortality. Thus, assessments of risk of bias resulting from lack of blinding may need to be made separately for different outcomes. Rather than assessing risk of bias for each outcome separately, it is often convenient to group outcomes with similar risks of bias. For example, there may be a common assessment for all subjective outcomes (eg, quality of life) that is different from objective outcomes (eg, blood work). 11

In summary, when considering the effectiveness of blinding in reducing the risk of bias, it is important to consider specifically:

Were the participants and study personnel blinded or not blinded?

Who assessed the outcomes and were they blinded or not blinded?

What was the risk of bias in the outcome assessment considering the subjectivity or objectivity of an outcome? 11

Hróbjartsson A ,
Jørgensen KJ ,
Schulz KF ,
Chalmers I ,
Karanicolas PJ ,
Farrokhyar F ,
Boutron I ,
Estellat C ,
Guittet L ,
Fergusson D ,
Forfang E ,
Higgins JPT ,

Competing interests None

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Double-Blind Experimental Study And Procedure Explained

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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What is a Blinded Study?

Binding, or masking, refers to withholding information regarding treatment allocation from one or more participants in a clinical research study, typically in randomized control trials .
A blinded study prevents the participants from knowing about their treatment to avoid bias in the research. Any information that can influence the subjects is withheld until the completion of the research.
Blinding can be imposed on any participant in an experiment, including researchers, data collectors, evaluators, technicians, and data analysts.
Good blinding can eliminate experimental biases arising from the subjects’ expectations, observer bias, confirmation bias, researcher bias, observer’s effect on the participants, and other biases that may occur in a research test.
Studies may use single-, double- or triple-blinding. A trial that is not blinded is called an open trial.

Double-Blind Studies

Double-blind studies are those in which neither the participants nor the experimenters know who is receiving a particular treatment.

Double blinding prevents bias in research results, specifically due to demand characteristics or the placebo effect.

Demand characteristics are subtle cues from researchers that can inform the participants of what the experimenter expects to find or how participants are expected to behave.

If participants know which group they are assigned to, they might change their behavior in a way that would influence the results. Similarly, if a researcher knows which group a participant is assigned to, they might act in a way that reveals the assignment or influences the results.

Double-blinding attempts to prevent these risks, ensuring that any difference(s) between the groups can be attributed to the treatment.

On the other hand, single-blind studies are those in which the experimenters are aware of which participants are receiving the treatment while the participants are unaware.

Single-blind studies are beneficial because they reduce the risk of errors due to subject expectations. However, single-blind studies do not prevent observer bias, confirmation bias , or bias due to demand characteristics.

Because the experiments are aware of which participants are receiving which treatments, they are more likely to reveal subtle clues that can accidentally influence the research outcome.

Double-blind studies are considered the gold standard in research because they help to control for experimental biases arising from the subjects’ expectations and experimenter biases that emerge when the researchers unknowingly influence how the subjects respond or how the data is collected.

Using the double-blind method improves the credibility and validity of a study .

Example Double-Blind Studies

Rostock and Huber (2014) used a randomized, placebo-controlled, double-blind study to investigate the immunological effects of mistletoe extract. However, their study showed that double-blinding is impossible when the investigated therapy has obvious side effects.

Using a double-blind study, Kobak et al. (2005) found that S t John’s wort ( Hypericum perforatum ) is not an efficacious treatment for anxiety disorder, specifically OCD.

Using the Yale–Brown Obsessive–Compulsive Scale (Y-BOCS), they found that the mean change with St John’s wort was not significantly different from the mean change found with placebo.

Cakir et al. (2014) conducted a randomized, controlled, and double-blind study to test the efficacy of therapeutic ultrasound for managing knee osteoarthritis.

They found that all assessment parameters significantly improved in all groups without a significant difference, suggesting that therapeutic ultrasound provided no additional benefit in improving pain and functions in addition to exercise training.

Using a randomized double-blind study, Papachristofilou et al. (2021) found that whole-lung LDRT failed to improve clinical outcomes in critically ill patients admitted to the intensive care unit requiring mechanical ventilation for COVID-19 pneumonia.

Double-Blinding Procedure

Double blinding is typically used in clinical research studies or clinical trials to test the safety and efficacy of various biomedical and behavioral interventions.

In such studies, researchers tend to use a placebo. A placebo is an inactive substance, typically a sugar pill, that is designed to look like the drug or treatment being tested but has no effect on the individual taking it.

The placebo pill was given to the participants who were randomly assigned to the control group. This group serves as a baseline to determine if exposure to the treatment had any significant effects.

Those randomly assigned to the experimental group are given the actual treatment in question. Data is collected from both groups and then compared to determine if the treatment had any impact on the dependent variable.

All participants in the study will take a pill or receive a treatment, but only some of them will receive the real treatment under investigation while the rest of the subjects will receive a placebo.

With double blinding, neither the participants nor the experimenters will have any idea who receives the real drug and who receives the placebo.

For Example

A common example of double-blinding is clinical studies that are conducted to test new drugs.

In these studies, researchers will use random assignment to allocate patients into one of three groups: the treatment/experimental group (which receives the drug), the placebo group (which receives an inactive substance that looks identical to the treatment but has no drug in it), and the control group (which receives no treatment).

Both participants and researchers are kept unaware of which participants are allocated to which of the three groups.

The effects of the drug are measured by recording any symptoms noticed in the patients.

Once the study is unblinded, and the researchers and participants are made aware of who is in which group, the data can be analyzed to determine whether the drug had effects that were not seen in the placebo or control group, but only in the experimental group.

Double-blind studies can also be beneficial in nonmedical interventions, such as psychotherapIes.

Reduces risk of bias

Double-blinding can eliminate, or significantly reduce, both observer bias and participant biases.

Because both the researcher and the subjects are unaware of the treatment assignments, it is difficult for their expectations or behaviors to influence the study.

Results can be duplicated

The results of a double-blind study can be duplicated, enabling other researchers to follow the same processes, apply the same test item, and compare their results with the control group.

If the results are similar, then it adds more validity to the ability of a medication or treatment to provide benefits.

It tests for three groups

Double-blind studies usually involve three groups of subjects: the treatment group, the placebo group, and the control group.

The treatment and placebo groups are both given the test item, although the researcher does not know which group is getting real treatment or placebo treatment.

The control group doesn’t receive anything because it serves as the baseline against which the other two groups are compared.

This is an advantage because if subjects in the placebo group improved more than the subjects in the control group, then researchers can conclude that the treatment administered worked.

Applicable across multiple industries

Double-blind studies can be used across multiple industries, such as agriculture, biology, chemistry, engineering, and social sciences.

Double-blind studies are used primarily by the pharmaceutical industry because researchers can look directly at the impact of medications.

Disadvantages

Inability to blind.

In some types of research, specifically therapeutic, the treatment cannot always be disguised from the participant or the experimenter. In these cases, you must rely on other methods to reduce bias.

Additionally, imposing blinding may be impossible or unethical for some studies.

Double-blinding can be expensive because the researcher has to examine all the possible variables and may have to use different groups to gather enough data.

Small Sample Size

Most double-blind studies are too small to provide a representative sample. To be effective, it is generally recommended that double-blind trials include around 100-300 participants.

Studies involving fewer than 30 participants generally can’t provide proof of a theory.

Negative Reaction to Placebo

In some instances, participants can have adverse reactions to the placebo, even producing unwanted side effects as if they were taking a real medication.

It doesn’t reflect real-life circumstances

When participants receive treatment or medication in a double-blind placebo study, each individual is told that the item in question might be real medication or a placebo.

This artificial situation does not represent real-life circumstances because when a patient receives a pill after going to the doctor in the real-world, they are told that the product is actual medicine intended to benefit them.

When situations don’t feel realistic to a participant, then the quality of the data can decrease exponentially.

What is the difference between a single-blind, double-blind, and triple-blind study?

In a single-blind study, the experimenters are aware of which participants are receiving the treatment while the participants are unaware.

In a double-blind study, neither the patients nor the researchers know which study group the patients are in. In a triple-blind study, neither the patients, clinicians, nor the people carrying out the statistical analysis know which treatment the subjects had.

Is a double-blind study the same as a randomized clinical trial?

Yes, a double-blind study is a form of a randomized clinical trial in which neither the participants nor the researcher know if a subject is receiving the experimental treatment, a standard treatment, or a placebo.

Are double-blind studies ethical?

Double blinding is ethical only if it serves a scientific purpose. In most circumstances, it is unethical to conduct a double-blind placebo controlled trial where standard therapy exists.

What is the purpose of randomization using double blinding?

Randomization with blinding avoids reporting bias, since no one knows who is being treated and who is not, and thus all treatment groups should be treated the same. This reduces the influence of confounding variables and improves the reliability of clinical trial results.

Why are double-blind experiments considered the gold standard?

Randomized double-blind placebo control studies are considered the “gold standard” of epidemiologic studies as they provide the strongest possible evidence of causality.

Additionally, because neither the participants nor the researchers know who has received what treatment, double-blind studies minimize the placebo effect and significantly reduce bias.

Can blinding be used in qualitative studies?

Yes, blinding is used in qualitative studies .

Cakir, S., Hepguler, S., Ozturk, C., Korkmaz, M., Isleten, B., & Atamaz, F. C. (2014). Efficacy of therapeutic ultrasound for the management of knee osteoarthritis: a randomized, controlled, and double-blind study. American journal of physical medicine & rehabilitation , 93 (5), 405-412.

Kobak, K. A., Taylor, L. V., Bystritsky, A., Kohlenberg, C. J., Greist, J. H., Tucker, P., … & Vapnik, T. (2005). St John’s wort versus placebo in obsessive–compulsive disorder: results from a double-blind study. International Clinical Psychopharmacology , 20 (6), 299-304.

Papachristofilou, A., Finazzi, T., Blum, A., Zehnder, T., Zellweger, N., Lustenberger, J., … & Siegemund, M. (2021). Low-dose radiation therapy for severe COVID-19 pneumonia: a randomized double-blind study. International Journal of Radiation Oncology* Biology* Physics , 110 (5), 1274-1282. Rostock, M., & Huber, R. (2004). Randomized and double-blind studies–demands and reality as demonstrated by two examples of mistletoe research. Complementary Medicine Research , 11 (Suppl. 1), 18-22.

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Double Blind Study – Blinded Experiments

In science and medicine, a blind study or blind experiment is one in which information about the study is withheld from the participants until the experiment ends. The purpose of blinding an experiment is reducing bias, which is a type of error . Sometimes blinding is impractical or unethical, but in many experiments it improves the validity of results. Here is a look at the types of blinding and potentials problems that arise.

Single Blind, Double Blind, and Triple Blind Studies

The three types of blinding are single blinding, double blinding, and triple blinding:

Single Blind Study

In a single blind study , the researchers and analysis team know who gets a treatment, but the experimental subjects do not. In other words, the people performing the study know what the independent variable is and how it is being tested. The subjects are unaware whether they are receiving a placebo or a treatment. They may even be unaware what, exactly, is being studied.

Example: Violin Study

For example, consider an experiment that tests whether or not violinists can tell the difference a Stradivarius violin (generally regarded as superior) and a modern violin. The researchers know the type of violin they hand to a violinist, but the musician does not (is blind). In case you’re curious, in an actual experiment performed by Claudia Fritz and Joseph Curtin, it turned out violinists actually can’t tell the instruments apart.

Double Blind Study

In a double blind study, neither the researchers nor subjects know which group receives a treatment and which gets a placebo .

Example: Drug Trial

Many drug trials are double-blinded, where neither the doctor nor patient knows whether the drug or a placebo is administered. So, who gets the drug or the placebo is randomly assigned (without the doctor knowing who gets what). The inactive ingredients, color, and size of a pill (for example) are the same whether it is the treatment or placebo.

Triple Blind Study

A triple blind study includes an additional level of blinding. So, the data analysis team or the group overseeing an experiment is blind, in addition to the researchers and subjects.

Example: Vaccine Study

Triple blind studies are common as part of the vaccine approval process. Here, the people who analyze vaccine effectiveness collate data from many test sites and are unaware of which group a participant belongs to.

Some guidelines advocate for removing terms like “single blind” and “double blind” because they do not inherently describe which party is blinded. For example, a double blind study could mean the subjects and scientists are blind or it could mean the subjects and assessors are blind. When you describe blinding in an experiment, report who is blinded and what information is concealed.

The point of blinding is minimizing bias. Subjects have expectations if they know they receive a placebo versus a treatment. And, researchers have expectations regarding the expected outcome. For example, confirmation bias occurs when an investigator favors outcomes that support pre-existing research or the scientist’s own beliefs.

Unblinding is when masked information becomes available. In experiments with humans, intentional unblinding after a study concludes is typical. This way, a subject knows whether or not they received a treatment or placebo. Unblinding after a study concludes does not introduce bias because the data has already been collected and analyzed.

However, premature unblinding also occurs. For example, a doctor reviewing bloodwork often figures out who is getting a treatment and who is getting a placebo. Similarly, patients feeling an effect from a pill or injection suspect they are in the treatment group. One safeguard against this is an active placebo. An active placebo causes side effects, so it’s harder to tell treatment and placebo groups apart just based on how a patient feels.

Although premature unblinding affects the outcome of the results, it isn’t usually reported. This is a problem because unintentional unblinding favors false positives, at least in medicine. For example, if subjects believe they are receiving treatment, they often feel better even if a therapy isn’t effective. Premature unblinding is one of the issues at the heart of the debate about whether or not antidepressants are effective. But, it applies to all blind studies.

Uses of Blind Studies

Of course, blind studies are valuable in medicine and scientific research. But, they also have other applications.

For example, in a police lineup, having an officer familiar with the suspects can influence a witness’s selection. A better option is a blind procedure, using an office who does not know a suspect’s identity. Product developers routinely use blind studies for determining consumer preference. Orchestras use blind judging for auditions. Some employers and educational institutions use blind data for application selection.

Bello, Segun; Moustgaard, Helene; Hróbjartsson, Asbjørn (October 2014). “The risk of unblinding was infrequently and incompletely reported in 300 randomized clinical trial publications”. Journal of Clinical Epidemiology . 67 (10): 1059–1069. doi: 10.1016/j.jclinepi.2014.05.007
Daston, L. (2005). “Scientific Error and the Ethos of Belief”. Social Research . 72 (1): 18. doi: 10.1353/sor.2005.0016
MacCoun, Robert; Perlmutter, Saul (2015). “Blind analysis: Hide results to seek the truth”. Nature . 526 (7572): 187–189. doi: 10.1038/526187a
Moncrieff, Joanna; Wessely, Simon; Hardy, Rebecca (2018). “Meta-analysis of trials comparing antidepressants with active placebos”. British Journal of Psychiatry . 172 (3): 227–231. doi: 10.1192/bjp.172.3.227
Schulz, Kenneth F.; Grimes, David A. (2002). “Blinding in randomised trials: hiding who got what”. Lancet . 359 (9307): 696–700. doi: 10.1016/S0140-6736(02)07816-9

Parameters for protecting the rights and welfare of participants in human subjects research studies.

Information Blocking in a Blinded Study

Single Blind Studies : A research study done in such a way that the participants do not know (are blinded to) what treatment they are receiving to ensure the study results are not biased (the power of suggestion). Blinded studies are sometimes called “ masked studies.”

Double Blind Studies : A research study in which both the participants participating and the researchers/investigators conducting the study are unaware of what treatment the participants are receiving.

In a few blinded studies, the results of research tests entered into MiChart Electronic Health Record (EHR) could ‘unblind’ a participant by giving clues as to the research intervention arm assigned.

Limiting a Research Participant's View of MyUofMHealth (Patient Portal)

Participants are unable to view radiology or lab test results that have been formally associated to the research study in MiChart. Results with no research study association will be visible to the participant. Participants will still be able to utilize all other functionality of the Patient Portal such as refilling prescriptions, paying bills, setting up appointments, etc. It is not possible to shield outpatient medication lists from the participant’s view at this time. If viewing their outpatient medication list would unblind participants, instruct them to avoid viewing the medication list for the necessary time period.

For all research studies, “Research Visit Notes” entered in MiChart are not visible to patients during the conduct of a study. HITS is developing the functionality to release Research Visit Notes and any previously shielded test results when a study is finished.

Note that if a research note is inadvertently entered as a clinical Progress Note, it will be shared, by default, once signed by the provider.

What actions should be taken to prevent participants from seeing their test results?

Request a Patient Portal restriction only if the results of tests could ‘unblind’ participants as to their research intervention assignment.

Before the study starts enrolling and entering orders in MiChart:

Submit a Ticket for MiChart Research IT service through the MichMed ServiceNow portal. Include the study’s HUM# in the request. Explain that the study requires lab test results and/or radiology test results to be restricted from access in the Patient Portal. An expected End Date for the restrictions will be helpful, but is not required.
Maintain accurate enrollment records in OnCore , because the interface between OnCore and MiChart allows for associating MiChart entries for a participant with the research study.
When you place an order for a research radiology or lab test, associate the order to the research study . This must be completed before the order is signed.

A study can request radiology and/or lab test order blocking in the portal after enrollment has begun, but every effort should be made to address it prior to participant enrollment. If a HUM# has ‘results blocking’ added after participants are enrolled:

Orders previously associated to the study will be retroactively blocked.
Prior orders not associated to the study cannot be blocked.

What IRBMED procedures do I need to follow when a research study limits access to the Patient Portal?

Use the “Required language for blinded studies” paragraphs available in the Standard Informed Consent Template , alerting participants to this temporary limitation on viewing results in the Patient Portal.
Identify the study need in eResearch application section 1-2.

res_irbmed_eResearch.Application.Question1-2.9.png

eResearch HUM application question 1-2.9 asks whether Patient Portal blinding is necessary

2800 Plymouth Road, Building 520, Room 3214, Ann Arbor, MI 48109-2800

A list of IRBMED staff is available in the Personnel Directory , or view the list of Regulatory Teams.

Edited By: [email protected] Last Updated: June 4, 2021 3:30 PM

Global estimates on the number of people blind or visually impaired by diabetic retinopathy: a meta-analysis from 2000 to 2020

Vision Loss Expert Group of the Global Burden of Disease Study &

the GBD 2019 Blindness and Vision Impairment Collaborators

Eye ( 2024 ) Cite this article

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Epidemiology
Retinal diseases

To estimate global and regional trends from 2000 to 2020 of the number of persons visually impaired by diabetic retinopathy and their proportion of the total number of vision-impaired individuals.

Data from population-based studies on eye diseases between 1980 to 2018 were compiled. Meta-regression models were performed to estimate the prevalence of blindness (presenting visual acuity <3/60) and moderate or severe vision impairment (MSVI; <6/18 to ≥3/60) attributed to DR. The estimates, with 95% uncertainty intervals [UIs], were stratified by age, sex, year, and region.

In 2020, 1.07 million (95% UI: 0.76, 1.51) people were blind due to DR, with nearly 3.28 million (95% UI: 2.41, 4.34) experiencing MSVI. The GBD super-regions with the highest percentage of all DR-related blindness and MSVI were Latin America and the Caribbean (6.95% [95% UI: 5.08, 9.51]) and North Africa and the Middle East (2.12% [95% UI: 1.55, 2.79]), respectively. Between 2000 and 2020, changes in DR-related blindness and MSVI were greater among females than males, predominantly in the super-regions of South Asia (blindness) and Southeast Asia, East Asia, and Oceania (MSVI).

Conclusions

Given the rapid global rise in diabetes and increased life expectancy, DR is anticipated to persist as a significant public health challenge. The findings emphasise the need for gender-specific interventions and region-specific DR healthcare policies to mitigate disparities and prevent avoidable blindness. This study contributes to the expanding body of literature on the burden of DR, highlighting the need for increased global attention and investment in this research area.

What colour are your eyes? Teaching the genetics of eye colour & colour vision. Edridge Green Lecture RCOphth Annual Congress Glasgow May 2019

A foundation model for generalizable disease detection from retinal images

How do I recognise and manage visual snow syndrome?

Introduction.

Diabetes mellitus (DM) and its complications are a major burden of disease around the world. DM has increased significantly in recent decades and will continue to rise in the next few decades, with a greater burden expected in low-middle income countries (LMICs) [ 1 ]. One of the most common microvascular complications of DM is diabetic retinopathy (DR). According to previous large-population based studies and meta-analyses, DR has been recognized as one of the most common causes of blindness and vision impairment among the working-age population; however, this is not true for some countries, such as the United Kingdom, due to the implementation of national DR strategies aimed at identifying and treating patients with this condition [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. The Global Burden of Disease Study (GBD) began 30 years ago to systematically assess and scientifically report on critical health outcomes including DM and its complications. The findings are reported longitudinally and across various populations [ 10 ]. In 2020, DR was listed as one of the leading causes of global blindness among those aged 50 years and above [ 3 ]. Leasher et al. assessed changes in the prevalence of DR-related blindness and moderate or severe vision impairment (MSVI) from 1990 to 2010 [ 8 ]. Findings showed that DR accounted for 2.6% of all blindness and 1.9% of all MSVI in 2010, an increase from 2.1% and 1.3%, respectively, from 1990 [ 8 ]. Early detection and treatment interventions for DR can reduce the risk of severe visual loss by approximately 90% [ 11 ].

The Lancet Global Health Commission emphasised how improving eye health contributes to achieving the sustainable development goals (SDGs) of improving general health and well-being, reducing poverty and increasing work productivity, and improving education and equity [ 7 ]. Due to the unmet need of an ageing and growing population globally, eye health is a major public health concern that requires urgent attention to develop innovative treatments and deliver services on a large scale. Political commitment is necessary to act on eye health, particularly in low-resource settings [ 7 , 12 ].

The current meta-analysis provides an update of all available population-based studies from 2000 to 2020 to present estimates on the number of people (aged 50 years+) affected by DR-related blindness and DR-related MSVI. Additionally, we investigate the global and regional differences in the prevalence of DR-related blindness and MSVI, and differences by sex.

Materials/subjects and methods

Preparation of data included first a systematic review of published (between Jan 1, 1980, and Oct 1, 2018) population-based studies of vision impairment and blindness by the Vision Loss and Expert Group (VLEG) that also included gray literature sources. Eligible studies from this review were then combined with data from Rapid Assessment of Avoidable Blindness (RAAB) studies. Data from the US National Health and Nutrition Examination survey and the World Health Organization (WHO) Study on Global Ageing and Adult Health were contributed by the GBD team. More detailed methods are published elsewhere [ 3 , 13 ] and briefly discussed as follows.

In total, VLEG identified 137 studies and extracted data from 70 studies in their 2010 review, and additional 67 studies in their 2014–18 review. Studies were primarily national and subnational cross-sectional surveys. Additionally, the VLEG commissioned the preparation of 5-year age-disaggregated RAAB data from the RAAB repository. Studies were included if they met the following criteria: visual acuity data had to be measured using a test chart that could be mapped to the Snellen scale, and the sample had to be representative of the population. Self-report of vision loss was excluded. We used International Classification of Diseases 11 th (ICD-11) edition criteria for vision impairment, as used by WHO, which categorises people according to vision in the better eye on presentation, in which moderate vision impairment is defined as a visual acuity of 6/60 or better but less than 6/18, severe vision impairment as a visual acuity of 3/60 or better but less than 6/60, and blindness as a visual acuity of less than 3/60 or less than 10° visual field around central fixation (although the visual field definition is rarely used in population-based eye surveys) [ 14 ].

First, we separated raw data into vision-loss envelopes for all-cause mild, moderate, and severe vision impairment, and blindness. Data were input into a mixed-effects meta-regression tool developed by the Institute for Health Metrics and Evaluation (IHME) called MR-BRT (meta regression; Bayesian; regularized; trimmed) [ 15 ]. Presenting vision impairment was the reference definition for each level of severity. Undercorrected refractive error data were extracted directly from data sources where available, and otherwise calculated by subtracting best-corrected vision impairment from presenting vision impairment prevalence for each level of severity in studies that reported both measures for a given location, sex, age group, and year. All other causes were quantified as part of the best-corrected estimates of vision impairment at each level of severity.

We modeled distance vision impairment and blindness due to the following causes: cataract, undercorrected refractive error, age-related macular degeneration, myopic macular degeneration, glaucoma, diabetic retinopathy, and other causes of vision impairment (in aggregate). Minimum age for inclusion of data for these causes was set at 20 years for cataract and diabetic retinopathy, and 45 years for glaucoma and age-related macular degeneration. Other vision impairment estimates were combined with less prevalent causes of vision impairment to create a residual category (e.g., retinopathy of prematurity, corneal opacities or optic atrophy, trachoma).

We produced location, year, age, and sex-specific estimates of MSVI and blindness using Disease Modeling Meta-Regression (Dismod-MR) 2.1 [ 16 ]. The data processing steps are described elsewhere [ 3 ]. Briefly, Dismod-MR 2.1 models were run for all vision impairment by severity (moderate, severe, blindness) regardless of cause and, separately, for MSVI and blindness due to each modeled cause of vision impairment (e.g., MSVI due to cataract and blindness due to cataract). Then, models of MSVI due to specific causes were split into moderate and severe estimates using the ratio of overall prevalence in the all-cause moderate presenting vision impairment and severe presenting vision impairment models. Next, prevalence estimates for all causes by severity were scaled to the models of all-cause prevalence by severity. This produced final estimates by age, sex, year, and location for each individual cause of vision impairment by severity. We age-standardized our estimates using the GBD standard population [ 17 ].

According to our estimates from 2020, approximately 1.07 million (95% uncertainty intervals (UIs): 0.76, 1.51) people were blind and nearly 3.28 million (95% UI: 2.41, 4.34) had MSVI globally due to DR (Table 1 ). An estimated 462,000 males and 611,000 females of all ages, and 368,000 males and 494,000 females aged ≥50 years had DR-related blindness in 2020 (Table 2 ). The number of males and females (all ages) with DR-related MSVI in 2020 was 1.4 million and 1.8 million, respectively, whereas an estimated 1.3 million and 1.7 million people were aged 50 years and over (Table 3 ). Higher prevalence rates of DR-related blindness were seen among females aged 60 years and above, with the highest rates observed in people aged >95 years. Higher prevalence rates of DR-related blindness and MSVI were seen among females aged 60 years and above, with the highest rates observed in females aged >95 years.

DR caused 2.50% (95% UI: 1.77, 3.52) of blindness in 2020 worldwide. Regionally, the highest percentage of all DR-related blindness was found in Latin America and Caribbean (6.95% [95% UI: 5.08, 9.51]) and High-Income super-regions (5.37% [95% UI: 3.86, 7.55]) (Table 1 ). The super-regions with the lowest percentage of all DR-related blindness were Central Europe, Eastern Europe, and Central Asia (0.97% [95% UI: 0.67, 1.39]), and Sub-Saharan Africa (0.98% [95% UI: 0.69, 1.40]). DR caused 1.11% (95% UI: 0.82, 1.47) of MSVI in 2020 worldwide. North Africa and Middle East (2.12% [95% UI: 1.55, 2.79]), and Latin America and Caribbean (1.84% [95% UI: 1.36, 2.45]) were super-regions with the highest percentage of all MSVI due to DR (Table 1 ).

In 2020, the global age-standardized prevalence of DR-related blindness in those aged ≥50 years was 0.05% (95% UI: 0.03, 0.07) and 0.16% (95% UI: 0.12, 0.21) for DR-related MSVI (Table 1 ). The super-region with the highest age-standardized prevalence of DR-related blindness was Latin American and Caribbean (0.15% [95% UI: 0.10, 0.21]). The lowest age-standardized prevalence of DR-related blindness in 2020 was in Central Europe, Eastern Europe, and Central Asia (0.01% [95% UI: 0.01, 0.01]). The super-regions with the highest age-standardized prevalence of DR-related MSVI in 2020 were North Africa and Middle East (0.41% [95% UI: 0.30, 0.55]), and Latin America and the Caribbean (0.30% [95% UI: 0.22, 0.40]). The lowest estimates were found in the High-Income (0.08% [95% UI: 0.06, 0.11]) and Central Europe, Eastern Europe, and Central Asia (0.09% [95% UI: 0.07, 0.13]) super-regions (Table 1 ). Figure 1 presents the crude prevalence of blindness and MSVI due to DR in 2020 across super-regions.

Crude prevalence of blindness and MSVI due to DR in 2020 by age, across seven world GBD super-regions. a Crude prevalence of blindness due to DR in 2020 by seven world GBD super-regions by age. The graph demonstrates an increase in prevalence with age, with notable variations between super-regions. The super-regions are represented by different coloured lines. b Crude prevalence of MSVI in 2020 by seven world GBD super-regions by age. Similar to ( a ), the prevalence increases with age, highlighting disparities among different super-regions. Each super-region is depicted by a distinct coloured line.

Between 2000 and 2020, the global percentage change in age-standardized prevalence of DR-related blindness among adults (≥50 years) showed different trends for males and females (Supplementary file, Table S1 ). For males, there was a minimal decrease of −0.10% (95% UI: −0.54, 0.34), while females experienced an increase of +12.89% (95% UI: 12.40, 13.38). An overall increase in the age-standardized prevalence of DR-related blindness among adults aged ≥50 years (both sexes) was found in South Asia (+25.66% [95% UI: 25.07, 26.24]), Southeast Asia, East Asia and Oceania (+15.36% [95% UI: 14.80, 15.92]) and Sub-Saharan Africa (+2.47% [95% UI: 2.01, 2.94]). An increase of +14.92% (95% UI: 14.39, 15.45) in age-standardized prevalence of DR-related blindness in South Asia from 2000 to 2020 was observed for males, whiles females experienced even greater gains with a rise of +34.68% (95% UI: 34.04, 35.32). In Southeast Asia, East Asia, and Oceania, the increase in age-standardized prevalence of DR-related blindness from 2000 to 2020 was +3.43% (95% UI: 2.94, 3.91) for males, compared to +26.34% (95% UI: 25.72, 26.97) for females. In Sub-Saharan Africa, although the overall age-standardized prevalence of DR-related blindness from 2000 to 2020 increased, a decrease was found among males (−12.46% [95% UI: −12.87, −12.04]) compared to females (+16.79% [95% UI: 16.27, 17.30]). All other super-regions demonstrated a decrease in the age-standardized prevalence of DR-related blindness (≥50 years) from 2000 to 2020 overall. In Central Europe, Eastern Europe and Central Asia, the age-standardized prevalence of DR-related blindness decreased by −21.99% (95% UI: −22.41, −21.58) for males compared to −3.15% (95% UI: −3.61, −2.70) for females. In Latin America and Caribbean, a decrease of −20.74% (95% UI: −21.06, −20.41) was observed in males, with a smaller decrease (−5.49% [95% UI: −5.86, −5.11]) among females. In the High-Income super-region, a reduction of −15.73% (95% UI: −16.13,−15.32) and −8.46% (95% UI: −8.83, −8.09) was found in males and females, respectively. Supplementary file contains Figs. ( S1 – S4 ) illustrating the total number of cases (males and females) with DR-related blindness and MSVI between 2000 and 2020, for all 21 GBD world regions, including the global total for comparison.

From 2000 to 2020, there was a decrease in the global percentage change in age-standardized prevalence of DR-related MSVI (≥50 years) among males (−0.93% [95% UI: −1.29, −0.56]), while females experienced an increase (+3.62% [95% UI: 3.25, 3.99]). Between 2000 and 2020, the super-region of Southeast Asia, East Asia, and Oceania showed an increase in the age-standardized prevalence of DR-related MSVI for both males (+1.17%, [95% UI: 0.79, 1.55]) and females (+3.33%, [95% UI: 2.95, 3.71]). In Sub-Saharan Africa, there was a decrease in the age-standardized prevalence of DR-related MSVI among males (−1.98%, [95% UI:−2.34, −1.63]), whereas females experienced an increase (+1.06%, [95% UI: 0.69, 1.42]). All other super-regions demonstrated a decrease in the age-standardized prevalence of DR-related MSVI (≥50 years) between 2000 and 2020 for both sexes. The super-region of North Africa and the Middle East showed the most notable decline in age-standardized DR-related MSVI for both sexes (−15.35% [−15.66, −15.05]). Among males, there was a decrease of −16.43% (95% UI: −16.73, −16.12), while females exhibited a −14.57% (95% UI: −14.88, −14.26) decrease (Supplementary file, Table S2 ).

The global percentage change in crude prevalence for DR-related blindness between 2000 and 2020 was +1.41% (95% UI: −0.96, 1.85) in males compared to a + 13.32% (95% UI: 12.83, 13.80) increase in females, and +7.90% (95% UI: 7.43, 8.36) overall. The percentage change in crude prevalence of DR-related MSVI was also higher among females (+3.56% (95% UI: 3.18, 3.93)) compared to males (+1.31% (95% UI: 0.93, 1.69)) globally (Supplementary file, Tables S1 , 2 ).

Although DR remains highly prevalent, the figures from 2020 show a slight decrease compared to those reported in 2010 [ 8 ]. In 2020, DR accounted for 2.5% of global blindness and 1.1% of MSVI, down from 2.6% and 1.9%, respectively, in 2010. Leasher et al. also showed that the highest age-standardized prevalence of DR-related blindness and MSVI was in the super-regions of North Africa/Middle East, Sub-Saharan Africa, and South Asia, while the lowest prevalence was in High-Income regions [ 8 ]. An increase in the numbers of people with DR-related blindness and MSVI with a relatively unchanged age-standardized prevalence from 2010 to 2020 may be attributed to the increasing population and average age in most regions, coupled with falling death rates.

Our study found that DR-related blindness has increased more among females than males in almost all super-regions. The largest sex-related inequalities were found in South Asia, Southeast Asia, East Asia and Oceania, and Sub-Saharan Africa. Though there are age-adjusted declines in DR prevalence for some super-regions, the overall global crude prevalence of both DR-related blindness and DR-related MSVI for males, females, and overall has increased globally due to aging and growth of the population. These figures represent the true burden of disease with which governments must contend.

The factors contributing to these gender disparities are multifaceted. One possible contributing factor is the difference in average life expectancy between women and men. As women tend to have a longer lifespan, they are consequently at greater risk of developing DM and DR. In LMICs, women may have poorer access to healthcare services compared to men [ 18 , 19 ]. Other factors that may contribute to disparities in eye health include, lack of access to information and resources, and lower literacy among females compared to males [ 20 , 21 , 22 ]. Pregnancy is another factor that can accelerate the progression of DR in women [ 23 ]. Finally, DR has been linked to intake of the retinal carotenoids lutein and zeaxanthin, and women are thought to have lower retinal levels of lutein and zeaxanthin [ 24 , 25 ]. The difference in retinal levels of lutein and zeaxanthin between men and women may be due to several factors including hormones, dietary patterns, and variances in metabolic processes [ 25 ]. Factors such as smoking might vary between women and men, contributing to differences in retinal levels. This requires further investigation to ascertain the precise causes behind the observed differences in retinal levels between men and women. Action is needed to improve female care and reduce the burden of DR-related blindness and MSVI.

Teo et al. estimated that there would be 103.12 million people with DR, 28.54 million people with vision-threatening DR, and 18.83 million people with clinically significant macular oedema in 2020 [ 26 ]. They found that the North America and Caribbean (NAC) and Middle East and North Africa (MENA) showed significantly higher prevalence of DR compared to other regions [ 26 ]. Similarly, our results show that the Latin America and Caribbean and North Africa, and Middle East super-regions demonstrated the highest prevalence of DR-related blindness and MSVI. This may be attributed to several factors such as limited access to quality healthcare services, increased DM cases, and inadequate management of DM. Although DR is estimated to affect over 100 million people globally, our data from 2020 suggests that less than 1.1 million are currently blind and less than 3.3 million are visually impaired. Compared to the 2010 data, 834,000 people were blind whereas 3.7 million were visually impaired [ 8 ]. The decline in the number of people with MSVI from 2010, despite an increase in DR-related blindness may be due to advancements in medical technology and treatments for DR. They play a role in preventing the progression of the disease to more severe stages, hence reducing the number of individuals with MSVI. Additionally, increased awareness about DM and its ocular complications might lead to earlier detection and intervention, which could prevent or mitigate MSVI cases despite the rise in DR-related blindness.

Blindness and MSVI can have a profound impact on quality of life, impairing both mental and physical health, and social independence [ 27 ]. As reported in the GBD Study 2019, blindness and low vision was ranked eighth (contributing 3·8% [95% UI 3·0, 4·9]) of all years lived with disability (YLDs) in people aged 50–69 years [ 13 ]. Among people aged 70 years and older, blindness and low vision was ranked fourth (contributing 6·4% [5·4, 7·4] of all YLDs) [ 13 ]. Furthermore, blindness and MSVI are associated with reduced economic, educational, and employment opportunities [ 28 , 29 , 30 ]. Economic productivity at the individual, family, community, and national level is important to sustainable development. An inability to work can diminish the productive capacity of the economy by reducing the workforce. Illness and disability can contribute to productivity losses through absenteeism from work, reduced productivity while at work or unemployment, including job loss and early retirement [ 28 , 29 , 30 , 31 ]. The Lancet Global Health Commission on Global Eye Health assessed the overall relative reduction in employment by working-aged people with blindness and MSVI [ 31 ]. They found that the global average relative reduction in employment of people with vision impairment was estimated to be 30.2% [ 31 ]. Since blindness and MSVI can have a large economic impact globally, more data on the employment status of people living with blindness and MSVI in all world regions, especially, LMICs needs to be available. Future research should explore more specifically how DR-related blindness and MSVI affect productivity losses and if there are relevant differences by sex.

We reviewed the literature to determine the economic burden of DR globally. According to UK estimates, DR has an annual cost of £379 million($476 million) for cases linked to type 2 DM, and almost £14 million ($17.6 million) for cases related to type 1 DM [ 32 ]. Economic modeling in the UK suggests that reducing the prevalence of type 2 DM-related DR by just 1% each year could save the UK economy £150 million ($188.6 million) by 2050 [ 32 ]. The estimated economic burden of DR in the United States is $0.5 billion [ 33 ], $3.91 billion in Germany [ 34 ], and $3.5 to 6.4 billion in the Latin America and the Caribbean region [ 35 ]. Further exploration of the economic burden in all world regions is necessary for agenda setting and policy planning in the future.

The VLEG populates and curates the Global Vision Database, a continuously updated, comprehensive, online database storing worldwide ophthalmic epidemiological information, including DR. By considering data from Jan 1st 1980 to Oct 1st 2018, the study covers a significant period, allowing for the assessment of trends and changes over time. The inclusion of gray literature enriches the database with unpublished data yet valuable data.

Our report provides an update on the worldwide and regional estimates for DR-related blindness and MSVI, including the changing patterns over time. It demonstrates that considerable regional differences and sex inequalities exist, highlighting areas that require particular attention such as low resource settings. These findings could aid further region-specific DR healthcare policies to prevent vision impairment, especially among females in the future.

Limitations

This meta-analysis has some limitations, such as potential publication bias and heterogeneity across studies. Due to the paucity of data across low burden regions, we may be over/under-estimating DR overall prevalence. While visual acuity is an important measure of visual function, it is not the only measure, and it is important to consider other methods of measuring visual impairment such as contrast sensitivity when assessing the prevalence of vision impairment. Nonetheless, our findings highlight the ongoing burden of DR-related vision impairment and underscore the need for effective prevention and management strategies.

Early detection and timely treatment are essential for preventing avoidable DR-related blindness and MSVI [ 36 , 37 ]. Between 2000 and 2020, high-income countries have made good progress in terms of reducing their DR-related blindness/MSVI which may be linked to improved risk factor control and advances in their screening and treatment services [ 7 , 38 , 39 ]. Despite this success, screening and treatment services still remain a challenge for super-regions such as Latin America (high prevalence of all DR-related blindness and MSVI ≥50 years old) [ 40 ]. While Sub-Saharan Africa might be anticipated to have a higher burden of DR compared to regions such as Latin America and Caribbean, Middle East, and North Africa, differences in population demographics, genetics, lifestyle, and DM management approaches contribute to varied prevalence rates. Under-reporting and insufficient data availability further complicate assessing the true extent of the issue. While healthcare resources are limited in Sub-Saharan Africa, certain areas within the region may have stronger healthcare infrastructure or targeted interventions that improve DR management compared to other LMICs. The global burden of DR is expected to remain high through 2045, disproportionately affecting countries in the Middle East and North Africa, and the Western Pacific [ 26 ]. Delivering innovative DR prevention and treatment strategies to improve global eye health is necessary. Screening for DR would also be much improved by the existence of population DM registers. Finally, our findings suggest the need for region-specific healthcare policies aimed at preventing vision loss, particularly among females.

Supplemental material is available at Eye’s website.

What was known before

Globally, in 2020, 1.07 million people were blind, and nearly 3.28 million were visually impaired by diabetic retinopathy.

What this study adds

The contribution of diabetic retinopathy and moderate and severe vision impairment (MSVI) by region and the change in this contribution between 2000 and 2020. The change in global age-standardized prevalence of DR-related blindness and MSVI between 2000 and 2020 and the differences by sex and region.

Data availability

The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the coordinator of the Vision Loss Expert Group (Professor Rupert Bourne; [email protected]) upon reasonable request. Data are located in controlled access data storage at Anglia Ruskin University.

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Acknowledgements

The funder of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

This study was funded by Brien Holden Vision Institute, Fondation Thea, Fred Hollows Foundation, Bill & Melinda Gates Foundation, Lions Clubs International Foundation (LCIF), Sightsavers International, and University of Heidelberg.

Author information

These authors contributed equally: Rupert R. A. Bourne, Jaimie D. Steinmetz.

Authors and Affiliations

Centre for Public Health, Queens University Belfast, Belfast, Northern Ireland

Katie Curran & Tunde Peto

Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany

Jost B. Jonas

Mass Eye and Ear, Harvard Medical School, Boston, MA, USA

David Friedman

University of Texas Southwestern Medical Center, Dallas, TX, USA

Judy E. Kim

Nova Southeastern University College for Optometry, Fort Lauderdale, FL, USA

Janet Leasher

Department of Ophthalmology, Cambridge University Hospitals, Cambridge, UK

Federal University of Sao Paolo, Sao Paolo/SP, Brazil

Arthur G. Fernandes

University of Calgary, Calgary/AB, Canada

School of Medicine, Vita-Salute San Raffaele University, Milan, Italy

Maria Vittoria Cicinelli

Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, Milan, Italy

Scientific Institute San Raffaele Hospital, Vita-Salute University, Milan, Italy

Alessandro Arrigo

University of Poitiers, Poitiers, France

Nicolas Leveziel

CHU de Poitiers, Poitiers, France

Brien Holden Vision Institute, Sydney, NSW, Australia

Serge Resnikoff

School of Optometry and Vision Sciences, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia

School of Population and Global Health, University of Melbourne, Carlton, VIC, Australia

Hugh R. Taylor

Vision and Eye Research Institute, Anglia Ruskin University, Cambridge, UK

Tabassom Sedighi, Rupert R. A. Bourne & Shahina Pardhan

Department of Computer Science, University of Oxford, Oxford, UK

Seth Flaxman

Ufa Eye Research Institute, Ufa, Russia

Mukkharram M. Bikbov

School of Life Course and Population Sciences, King’s College London, London, UK

Tasanee Braithwaite

The Medical Eye Unit, Guy’s and St Thomas’ NHS Foundation Trust, London, UK

University Hospital, Dijon, France

National University of Singapore, Singapore, Singapore

Ching-Yu Cheng

Singapore Eye Research Institute, Singapore, Singapore

University of Michigan, Ann Arbor, MI, USA

Monte A. Del Monte

Kellogg Eye Center, Ann Arbor, MI, USA

Institute for Social Research, University of Michigan, Ann Arbor, MI, USA

Joshua R. Ehrlich

Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, USA

Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil

João M. Furtado

Institute of Ophthalmology UCL & NIHR Biomedical Research Centre, London, UK

Gus Gazzard

Stanford University, Stanford, CA, USA

M. Elizabeth Hartnett

Associated Ophthalmologists of Monastir, Monastir, Tunisia

Rim Kahloun

Department of Ophthalmology, Harvard University, Boston, MA, USA

John H. Kempen

Eye Unit, MyungSung Medical College, Addis Ababa, Ethiopia

Department of Ophthalmology, Addis Ababa University, Addis Ababa, Ethiopia

Sight for Souls, Bellevue, WA, USA

Fattouma Bourguiba University Hospital, University of Monastir, Monastir, Tunisia

Moncef Khairallah

Allen Foster Community Eye Health Research Centre, Gullapalli Pratibha Rao International Centre for Advancement of Rural Eye care, L.V. Prasad Eye Institute, Hyderabad, India

Rohit C. Khanna

Brien Holden Eye Research Centre, L.V. Prasad Eye Institute, Banjara Hills, Hyderabad, India

School of Optometry and Vision Science, University of New South Wales, Sydney, NSW, Australia

Rohit C. Khanna & Kovin S. Naidoo

University of Rochester, School of Medicine and Dentistry, Rochester, NY, USA

HelpMeSee, Instituto Mexicano de Oftalmologia, Santiago de Querétaro, Mexico

Van Charles Lansingh

University of Miami, Miami, FL, USA

University of Utah, Salt Lake City, UT, USA

African Vision Research Institute, University of KwaZulu-Natal (UKZN), Durban, South Africa

Kovin S. Naidoo

Suraj Eye Institute, Nagpur, India

Vinay Nangia

Institute of Optics and Optometry, University of Social Science, 121 Gdanska str., Lodz, 90-519, Poland

Michal Nowak

Medicine & Health, University of New South Wales, Sydney, NSW, Australia

Konrad Pesudovs

John Hopkins Wilmer Eye Institute, Baltimore, MD, USA

Pradeep Ramulu

1st Department of Ophthamology, Medical School, Aristotle University of Thessaloniki, Ahepa Hospital, Thessaloniki, Greece

Fotis Topouzis

University of Crete Medical School, Giofirakia, Greece

Mitiadis Tsilimbaris

Beijing Institute of Ophthamology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthamology and Visual Sciences Key Laboratory, Beijing, China

Ya Xing Wang

Beijing Institute of Ophthamology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China

Ningli Wang

Centre for Public Health, Queen’s University Belfast, Belfast, UK

Vision and Eye Research Unit, Anglia Ruskin University, Cambridge, UK

Rupert Bourne

College of Optometry, Nova Southeastern University, Fort Lauderdale, FL, USA

Janet L. Leasher

Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland

Department of Ophthalmology, Heidelberg University, Mannheim, Germany

Mass Eye and Ear Department of Ophthalmology, Harvard Medical School, Boston, MA, USA

David S. Friedman

Department of Ophthalmology and Visual Sciences, Medical College of Wisconsin, Milwaukee, WI, USA

Department of Ophthalmology and Visual Sciences, Federal University of São Paulo, São Paulo, Brazil

School of Public Health, University of Technology Sydney, Sydney, NSW, Australia

Bright Opoku Ahinkorah

Ophthalmic Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Hamid Ahmadieh

Department of Ophthalmology, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Institute of Endemic Diseases, University of Khartoum, Khartoum, Sudan

Ayman Ahmed

Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland

Department of Ophthalmology, University of Leipzig Medical Center, Leipzig, Germany

Ahmad Samir Alfaar

Department of Ophthalmology, Charité Medical University Berlin, Berlin, Germany

Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Louay Almidani

Doheny Image Reading and Research Lab (DIRRL) - Doheny Eye Institute, University of California Los Angeles, Los Angeles, CA, USA

Department of Population and Behavioural Sciences, University of Health and Allied Sciences, Ho, Ghana

Department of Medicine, University of Thessaly, Volos, Greece

Sofia Androudi

Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran

Jalal Arabloo

Department of Applied Mathematics, University of Washington, Seattle, WA, USA

Aleksandr Y. Aravkin

Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA

Aleksandr Y. Aravkin, Paul Svitil Briant, Katrin Burkart, Kaleb Coberly, Xiaochen Dai, Stephen S. Lim, Tomislav Mestrovic, Ali H. Mokdad, Christopher J. L. Murray, Jaimie D. Steinmetz, Theo Vos & Peng Zheng

Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA

Aleksandr Y. Aravkin, Katrin Burkart, Xiaochen Dai, Stephen S. Lim, Awoke Misganaw, Ali H. Mokdad, Christopher J. L. Murray, Theo Vos & Peng Zheng

Department of Public Health, Debre Tabor University, Debre Tabor, Ethiopia

Mulu Tiruneh Asemu

Department of Neurovascular Research, Nested Knowledge, Inc., Saint Paul, MN, USA

Ahmed Y. Azzam

Faculty of Medicine, October 6 University, 6th of October City, Egypt

Department of Nursing, Saveh University of Medical Sciences, Saveh, Iran

Nayereh Baghcheghi

Big Data Institute - GRAM Project, University of Oxford, Oxford, UK

Freddie Bailey

Vocational School of Technical Sciences, Batman University, Batman, Türkiye

Mehmet Firat Baran

Miami Cancer Institute, Baptist Health South Florida, Miami, FL, USA

Mainak Bardhan

Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany

Till Winfried Bärnighausen

T.H. Chan School of Public Health, Harvard University, Boston, MA, USA

Department of Epidemiology, University of Florida, Gainesville, FL, USA

Amadou Barrow

Department of Public & Environmental Health, University of The Gambia, Brikama, The Gambia

Department of Community Medicine and Family Medicine, All India Institute of Medical Sciences, Jodhpur, India

Pankaj Bhardwaj

School of Public Health, All India Institute of Medical Sciences, Jodhpur, India

Epidemiology Department, Ufa Eye Research Institute, Ufa, Russia

Mukharram Bikbov

Ophthalmology Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK

International Centre for Eye Health, London School of Hygiene & Tropical Medicine, London, UK

Internal Medicine Department, Hospital Italiano de Buenos Aires (Italian Hospital of Buenos Aires), Buenos Aires, Argentina

Luis Alberto Cámera

Board of Directors, Argentine Society of Medicine, Buenos Aires, Argentina

Department of Addiction Medicine, Haukland University Hospital, Bergen, Norway

Omid Dadras

Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway

School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Amin Dehghan

USAID-JSI, Jimma University, Addis Ababa, Ethiopia

Berecha Hundessa Demessa

Department of Human Physiology, University of Gondar, Gondar, Ethiopia

Mengistie Diress

Department of Medicine, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Viet Nam

Thanh Chi Do

Department of Medicine, Can Tho University of Medicine and Pharmacy, Can Tho, Viet Nam

Thao Huynh Phuong Do

Department of Social Medicine and Health Care Organisation, Medical University “Prof. Dr. Paraskev Stoyanov”, Varna, Bulgaria

Klara Georgieva Dokova

Postgraduate Program in Epidemiology, Federal University of Rio Grande do Sul, Porto Alegre, Brazil

Bruce B. Duncan

Department of Epidemiology and Medical Statistics, University of Ibadan, Ibadan, Nigeria

Michael Ekholuenetale

Faculty of Public Health, University of Ibadan, Ibadan, Nigeria

Faculty of Medicine, University of Tripoli, Tripoli, Libya

Muhammed Elhadi

Ophthalmic Epidemiology Research Center, Shahroud University of Medical Sciences, Shahroud, Iran

Mohammad Hassan Emamian

Department of Ophthalmology, University of California Los Angeles, Los Angeles, CA, USA

Mehdi Emamverdi

School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

Hossein Farrokhpour & Sara Momtazmanesh

Endocrinology and Metabolism Research Institute, Non-Communicable Diseases Research Center (NCDRC), Tehran, Iran

Hossein Farrokhpour

Department of Environmental Health Engineering, Isfahan University of Medical Sciences, Isfahan, Iran

Ali Fatehizadeh

University Eye Clinic, University of Genoa, Genoa, Italy

Lorenzo Ferro Desideri

Division of Ophthalmology, University of São Paulo, Ribeirão Preto, Brazil

Department of Environmental Health, Wollo University, Dessie, Ethiopia

Mesfin Gebrehiwot

Ophthalmology Department, Tehran University of Medical Sciences, Tehran, Iran

Fariba Ghassemi

Department of Clinical Pharmacy, Haramaya University, Harar, Ethiopia

Mesay Dechasa Gudeta

Toxicology Department, Shriram Institute for Industrial Research, Delhi, India

Sapna Gupta

School of Medicine, Deakin University, Geelong, VIC, Australia

Veer Bala Gupta

Faculty of Medicine Health and Human Sciences, Macquarie University, Sydney, NSW, Australia

Vivek Kumar Gupta

Brain and Behavioral Sciences Program, University of Georgia, Athens, GA, USA

Billy Randall Hammond

Department of Nursing, Arak University of Medical Sciences, Arak, Iran

Mehdi Harorani

Department of Ophthalmology, Iran University of Medical Sciences, Karaj, Iran

Hamidreza Hasani

Independent Consultant, Santa Clara, CA, USA

Golnaz Heidari

Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam

Mehdi Hosseinzadeh

Department of Computer Science, University of Human Development, Sulaymaniyah, Iraq

Department of Ophthalmology and Visual Science, Yale University, New Haven, CT, USA

John J. Huang

Institute for Physical Activity and Nutrition, Deakin University, Burwood, VIC, Australia

Sheikh Mohammed Shariful Islam

Sydney Medical School, University of Sydney, Sydney, NSW, Australia

Student Research Committee, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Nilofer Javadi

Department of Pediatrics, Isfahan University of Medical Sciences, Isfahan, Iran

Department of Ocular Epidemiology and Visual Health, Institute of Ophthalmology Foundation Conde de Valencia, Mexico City, Mexico

Aida Jimenez-Corona

Directorate General of Epidemiology, Mexico City, Mexico

Zoonoses Research Center, Islamic Azad University, Karaj, Iran

Mohammad Jokar

Department of Clinical Sciences, Jahrom University of Medical Sciences, Jahrom, Iran

Department of Economics, National Open University, Benin City, Nigeria

Charity Ehimwenma Joshua

Department of Oral and Maxillofacial Pathology, Krishna Vishwa Vidyapeeth (Deemed to be University), Karad, India

Vidya Kadashetti

Save Sight Institute, University of Sydney, Sydney, NSW, Australia

Himal Kandel & Yuyi You

Sydney Eye Hospital, South Eastern Sydney Local Health District, Sydney, NSW, Australia

Himal Kandel

Eye Research Center, Iran University of Medical Sciences, Tehran, Iran

Hengameh Kasraei

Health Policy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

Department of Pharmacology, All India Institute of Medical Sciences, Jodhpur, India

Rimple Jeet Kaur

Research Department, Better Vision Foundation Nepal, Kathmandu, Nepal

Sudarshan Khanal

Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Zahra Khorrami

Social Determinants of Health Research Center, Saveh University of Medical Sciences, Saveh, Iran

Hamid Reza Koohestani

Department of Anthropology, Panjab University, Chandigarh, India

Kewal Krishan

Ophthalmology Department, Ministry of Health & Population, Aswan, Egypt

Mohammed Magdy Abd El Razek

Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran

Vahid Mansouri

Department GF Ingrassia, University of Catania, Catania, Italy

Andrea Maugeri

University Centre Varazdin, University North, Varazdin, Croatia

Tomislav Mestrovic

National Data Management Center for Health, Ethiopian Public Health Institute, Addis Ababa, Ethiopia

Awoke Misganaw

Optometry & Vision Sciences, Zahedan University of Medical Sciences, Zahedan, Iran

Hamed Momeni-Moghaddam

Eye Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

Non-communicable Diseases Research Center, Tehran University of Medical Sciences, Tehran, Iran

Sara Momtazmanesh

Department of Medical Laboratory Sciences, Adigrat University, Adigrat, Ethiopia

Hadush Negash

School of Medicine, Western Sydney University, Campbelltown, NSW, Australia

Uchechukwu Levi Osuagwu

Department of Optometry and Vision Science, University of KwaZulu-Natal, KwaZulu-Natal, South Africa

Global Health Governance Programme, University of Edinburgh, Edinburgh, UK

School of Dentistry, University of Leeds, Leeds, UK

Department of Genetics, Yale University, New Haven, CT, USA

Shrikant Pawar

Department of Statistics and Econometrics, Bucharest University of Economic Studies, Bucharest, Romania

Ionela-Roxana Petcu

Medical School, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Viet Nam

Hoang Tran Pham

Ophthalmology department, Isfahan University of Medical Sciences, Isfahan, Iran

Mohsen Pourazizi

Department of Neonatology, Case Western Reserve University, Cleveland, OH, USA

Ibrahim Qattea

Department of Population Science and Human Resource Development, University of Rajshahi, Rajshahi, Bangladesh

Mosiur Rahman

Multidisciplinary Laboratory Foundation University School of Health Sciences (FUSH), Foundation University, Islamabad, Pakistan

International Center of Medical Sciences Research (ICMSR), Islamabad, Pakistan

Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

Amirhossein Sahebkar

Biotechnology Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

Department of Medicine, Tehran University of Medical Sciences, Tehran, Iran

Mohammad Amin Salehi

Department of Ophthalmology, Harvard Medical School, Boston, MA, USA

Maryam Shayan

Ophthalmic Research Center (ORC), Shahid Beheshti University of Medical Sciences, Tehran, Iran

Department of Veterinary Public Health and Preventive Medicine, Usmanu Danfodiyo University, Sokoto, Sokoto, Nigeria

Aminu Shittu

Aier Eye Hospital, Jinan university, Guangzhou, China

1st Department of Ophthalmology, Aristotle University of Thessaloniki, Thessaloniki, Greece

Department of Medicine, University of Crete, Heraklion, Greece

Aristidis Tsatsakis

Medical Genomics Research Department, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia

Muhammad Umair

Department of Life Sciences, University of Management and Technology, Lahore, Pakistan

School of Public Health, Zhejiang University, Zhejiang, China

Department of Public Health Science, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

Macquarie Medical School, Macquarie University, Sydney, NSW, Australia

Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA

Mikhail Sergeevich Zastrozhin

Addictology Department, Russian Medical Academy of Continuous Professional Education, Moscow, Russia

School of Medicine, Wuhan University, Wuhan, China

Zhi-Jiang Zhang

Vision Loss Expert Group of the Global Burden of Disease Study

Katie Curran
, Tunde Peto
, Jost B. Jonas
, David Friedman
, Judy E. Kim
, Janet Leasher
, Ian Tapply
, Arthur G. Fernandes
, Maria Vittoria Cicinelli
, Alessandro Arrigo
, Nicolas Leveziel
, Serge Resnikoff
, Hugh R. Taylor
, Tabassom Sedighi
, Seth Flaxman
, Mukkharram M. Bikbov
, Tasanee Braithwaite
, Alain Bron
, Ching-Yu Cheng
, Monte A. Del Monte
, Joshua R. Ehrlich
, João M. Furtado
, Gus Gazzard
, M. Elizabeth Hartnett
, Rim Kahloun
, John H. Kempen
, Moncef Khairallah
, Rohit C. Khanna
, Van Charles Lansingh
, Kovin S. Naidoo
, Vinay Nangia
, Michal Nowak
, Konrad Pesudovs
, Pradeep Ramulu
, Fotis Topouzis
, Mitiadis Tsilimbaris
, Ya Xing Wang
, Ningli Wang
& Rupert R. A. Bourne
, Rupert Bourne
, Janet L. Leasher
, David S. Friedman
, Bright Opoku Ahinkorah
, Hamid Ahmadieh
, Ayman Ahmed
, Ahmad Samir Alfaar
, Louay Almidani
, Hubert Amu
, Sofia Androudi
, Jalal Arabloo
, Aleksandr Y. Aravkin
, Mulu Tiruneh Asemu
, Ahmed Y. Azzam
, Nayereh Baghcheghi
, Freddie Bailey
, Mehmet Firat Baran
, Mainak Bardhan
, Till Winfried Bärnighausen
, Amadou Barrow
, Pankaj Bhardwaj
, Mukharram Bikbov
, Paul Svitil Briant
, Katrin Burkart
, Luis Alberto Cámera
, Kaleb Coberly
, Omid Dadras
, Xiaochen Dai
, Amin Dehghan
, Berecha Hundessa Demessa
, Mengistie Diress
, Thanh Chi Do
, Thao Huynh Phuong Do
, Klara Georgieva Dokova
, Bruce B. Duncan
, Michael Ekholuenetale
, Muhammed Elhadi
, Mohammad Hassan Emamian
, Mehdi Emamverdi
, Hossein Farrokhpour
, Ali Fatehizadeh
, Lorenzo Ferro Desideri
, Mesfin Gebrehiwot
, Fariba Ghassemi
, Mesay Dechasa Gudeta
, Sapna Gupta
, Veer Bala Gupta
, Vivek Kumar Gupta
, Billy Randall Hammond
, Mehdi Harorani
, Hamidreza Hasani
, Golnaz Heidari
, Mehdi Hosseinzadeh
, John J. Huang
, Sheikh Mohammed Shariful Islam
, Nilofer Javadi
, Aida Jimenez-Corona
, Mohammad Jokar
, Charity Ehimwenma Joshua
, Vidya Kadashetti
, Himal Kandel
, Hengameh Kasraei
, Rimple Jeet Kaur
, Sudarshan Khanal
, Zahra Khorrami
, Hamid Reza Koohestani
, Kewal Krishan
, Stephen S. Lim
, Mohammed Magdy Abd El Razek
, Vahid Mansouri
, Andrea Maugeri
, Tomislav Mestrovic
, Awoke Misganaw
, Ali H. Mokdad
, Hamed Momeni-Moghaddam
, Sara Momtazmanesh
, Christopher J. L. Murray
, Hadush Negash
, Uchechukwu Levi Osuagwu
, Shahina Pardhan
, Jay Patel
, Shrikant Pawar
, Ionela-Roxana Petcu
, Hoang Tran Pham
, Mohsen Pourazizi
, Ibrahim Qattea
, Mosiur Rahman
, Umar Saeed
, Amirhossein Sahebkar
, Mohammad Amin Salehi
, Maryam Shayan
, Aminu Shittu
, Jaimie D. Steinmetz
, Aristidis Tsatsakis
, Muhammad Umair
, Hong Xiao
, Mikhail Sergeevich Zastrozhin
, Zhi-Jiang Zhang
& Peng Zheng

Contributions

Please see Appendix for more detailed information about individual author contributions to the research, divided into the following categories: managing the overall research enterprise; writing the first draft of the manuscript; primary responsibility for applying analytical methods to produce estimates; primary responsibility for seeking, cataloguing, extracting, or cleaning data; designing or coding figures and tables; providing data or critical feedback on data sources; developing methods or computational machinery; providing critical feedback on methods or results; drafting the manuscript or revising it critically for important intellectual content; and managing the estimation or publications process.

Corresponding author

Correspondence to Rupert R. A. Bourne .

Ethics declarations

Competing interests.

GBD 2019 Blindness and Vision Impairment Collaborators : T W Bärnighausen reports grants or contracts from the European Union (Horizon 2020 and EIT Health), German Research Foundation (DFG), US National Institutes of Health, German Ministry of Education and Research, Alexander von Humboldt Foundation, Else-Kröner-Fresenius-Foundation, Wellcome Trust, Bill & Melinda Gates Foundation, KfW, UNAIDS, and WHO; consulting fees from KfW on the OSCAR initiative in Vietnam; participation on a Data Safety Monitoring Board or Advisory Board with NIH-funded study “Healthy Options” (PIs: Smith Fawzi, Kaaya) as Chair of the Data Safety and Monitoring Board, German National Committee on the “Future of Public Health Research and Education” as Chair of the scientific advisory board to the EDCTP Evaluation, Member of the UNAIDS Evaluation Expert Advisory Committee, National Institutes of Health Study Section Member on Population and Public Health Approaches to HIV/AIDS (PPAH), US National Academies of Sciences, Engineering, and Medicine’s Committee for the “Evaluation of Human Resources for Health in the Republic of Rwanda under the President’s Emergency Plan for AIDS Relief (PEPFAR)”, University of Pennsylvania (UPenn) Population Aging Research Center (PARC) as an External Advisory Board Member; leadership or fiduciary roles in board, society, committee or advocacy groups, paid or unpaid with the Global Health Hub Germany (which was initiated by the German Ministry of Health) as co-chair; all outside the submitted work. R Bourne reports support for the present manuscript to their institution, supporting the Vision Loss Expert Group, from the World Health Organization, the Brien Holden Vision Institute, Foundation Thea, Fred Hollows Foundation, Lions Clubs International Foundation; Bourne reports grants or contracts outside the submitted work to their institution supporting the Vision Loss Expert Group from Sightsavers International and University of Heidelberg. X Dai reports support for the present manuscript from the Institute for Health Metrics and Evaluation (University of Washington) for their salary. D S Friedman reports grants or contracts to their institution for research from Genentech; consulting fees from Abbvie, Kaliyope, Life Biosciences, Bausch and Lomb; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Thea Pharmaceuticals; support for attending meetings and travels from Thea Pharmaceuticals; leadership or fiduciary roles in board, society, committee or advocacy groups, paid or unpaid with Orbis International as a Member of the Board of Governors; all outside the submitted work. J M Furtado reports consulting fees outside the submitted work from SightFirst Latin America and Pan American Health Organization. V B Gupta reports grants or contracts from National Health and Medical Research Council (NHMRC) provided to Deakin University; outside the submitted work. V K Gupta reports grants or contracts from National Health and Medical Research Council (NHMRC) provided to Macquarie University; outside the submitted work. S M S Islam reports support for the present manuscript from the National Health and Medical Research Council (NHMRC, Australia) via an investigator grant and from the Heart Foundation of Australia via a Vanguard Grant. J E Kim reports consulting fees from Allergan, Apellis, Astellas, Bausch&Lomb, Clearside Biomedical, DORC, Genentech/Roche, Notal Vision, Outlook Therapeutics, and Regeneron; leadership or fiduciary roles in board, society, committee or advocacy groups, unpaid, with American Society of Retina Specialists, Macula Society, American Academy of Ophthalmology, and NAEVR/AEVR; receipt of equipment for research from Optos; all outside the submitted work. K Krishan reports non-financial support from the UGC Centre of Advanced Study, CAS II, awarded to the Department of Anthropology, Panjab University (Chandigarh, India); outside the submitted work. J L Leasher reports leadership or fiduciary roles in board, society, committee or advocacy groups, unpaid as a member of the National Eye Institute National Eye Health Education Program planning committee; outside the submitted work. J D Steinmetz reports support for the present manuscript from the Bill and Melinda Gates Foundation IHME funding for GBD analyses. Y Tan reports support for the present manuscript from the Department of Ophthalmology, The Third Xiangya Hospital, Central South University and the Postdoctoral Station of Clinical Medicine, The Third Xiangya Hospital, Central South University. F Topouzis reports grants or contracts from Thea Pharma Inc., Omikron, Pfizer, Alcon, AbbVie, Bayer, paid to their institution; consulting fees paid to them from Thea Pharma Inc., Omikron, Bausch & Lomb; participation on a Data Safety Monitoring Board or Advisory Board with Omikron, paid to them, and AbbVie and Roche, paid to their institution; leadership or fiduciary roles in board, society, committee or advocacy groups, unpaid, with European Glaucoma Society as President, Greek Glaucoma Society as President, and World Glaucoma Association as member of the Board of Governors; all outside the submitted work. Vision Loss Expert Group of the Global Burden of Disease Study : A Bron reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Théa. M A Del Monte reports support for attending meetings and/or travel from the University of Michigan, and leadership or fiduciary roles in board, society, committee or advocacy groups, paid or unpaid as past president of Costenbader Society. D Friedman reports receipt of equipment, materials, drugs, medical writing, gifts or other services from LumenisCorp (instrumental loan). J M Furtado reports consulting fees from Pan American Health Organization and from Lions Club International Foundation. G Gazzard reports consulting fees from Alcon Laboratories, Inc; Allergan, Inc; BELKIN Vision LTD; Carl Zeiss Meditec; Elios; Genentech/Roche; Reichert; Théa and ViaLase; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Alcon Laboratories, Inc; BELKIN Vision Ltd; Carl Zeiss Meditec; Elios and Ellex; participation on a Data Safety Monitoring Board or Advisory Board with Alcon Laboratories, Inc; Allergan, Inc; BELKIN Vision Ltd; Carl Zeiss Meditec; Elios and Visufarma; and leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid with Glaucoma UK Medical Advisory Board and UK&Eire Glaucoma Society as president. M E Hartnett reports support for the present manuscript (e.g., funding, provision of study materials, medical writing, article processing charges, etc.) from Michael F. Marmor, M.D. Professor of Retinal Science and Disease as endowment to support salary; grants or contracts from any entity (from National Eye Institute R01 EY017011 and National Eye Institute R01 EY015130) as partial salary support; patents planned, issued or pending (WO2015123561A2 and WO2021062169A1); and leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid with Jack McGovern Coats’ Disease Foundation and as director of Women’s Eye Health and Macular Society Grant Review Chair. J H Kempen reports support for the present manuscript (e.g., funding, provision of study materials, medical writing, article processing charges, etc.) from Mass Eye and Ear Global Surgery Program (as support of salary); grants or contracts from any entity from Sight for Souls (as support of salary); and stock or stock options with Betaliq and Tarsier (both as small equity owner). J E Kim reports consulting fees from Genentech/Roche, DORC, Notal Vision and Outlook Therapeutics (all as payment to J E Kim); participation on a Data Safety Monitoring Board or Advisory Board with Allergan, Amgen, Apellis, Bausch&Lomb, Clearside, Coherus, Novartis and Regeneron (all as participation on advisory board); leadership or fiduciary role in other borad, society, committee or advocacy group, paid or unpaid, with AAO, APRIS, ASRS, Macular Society and NAEVR/AEVR (all unpaid); and receipt of equipment, materials, drugs, medical writing, gifts or other services from Clearside and Genentech/Roche (both for medical writing). V C Lansingh reports consulting fees from HelpMeSee (as an employee); and support for attending meetings and/or travel from HelpMeSee (pay airfare and hotel). J Leasher reports leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid with National Eye Institute (as a member) and National Eye Health Education Program planning committee (unpaid). M Nowak reports participation on a Data Safety Monitoring Board or Advisory Board with Vision Express Co. Poland as the chairman of medical advisory board of Vision Express Co. Poland. T Peto reports grants or contracts from any entity from Novartis (paid to institution); payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Bayer, Novartis and Roche (paid to T Peto); and leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid with European Association for Diabetic Eye Complications as current president (unpaid). P Ramulu reports grants or contracts from National Institute of Health and Perfuse Therapeutics; and consulting fees from Alcon and W. L. Gore. F Topouzis reports grants or contracts from Théa, Omikron, Pfizer, Alcon, Abbvie and Bayer (all paid to Institution); consulting fees from Omikron, Théa and Bausch & Lomb (all paid to Topouzis); payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Omikron (paid to Topouzis), Abbvie and Roche (both paid to Institute); and leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid with European Glaucoma Society (as president), Greek Glaucoma Society (as president) and Board of Governors, World Glaucoma Association (all unpaid).

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Supplementary information

Fig s1: number of males (all ages) with msvi due to diabetic retinopathy in 2000 and 2020 by 21 gbd world regions, fig s2: number of females (all ages) with msvi due to diabetic retinopathy in 2000 and 2020 by 21 gbd world regions, fig s3: number of males (all ages) with blindness due to diabetic retinopathy in 2000 and 2020 by 21 gbd world regions, 41433_2024_3101_moesm6_esm.pdf.

Fig S4: Number of females (all ages) with blindness due to Diabetic retinopathy in 2000 and 2020 by 21 GBD world regions

Appendix: Contributions by Authors

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Vision Loss Expert Group of the Global Burden of Disease Study., the GBD 2019 Blindness and Vision Impairment Collaborators. Global estimates on the number of people blind or visually impaired by diabetic retinopathy: a meta-analysis from 2000 to 2020. Eye (2024). https://doi.org/10.1038/s41433-024-03101-5

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How it works

Blinding and Its Types in Research

Published by Jamie Walker at August 26th, 2021 , Revised On July 5, 2022

What is Blinding?

Blinding in experimental research is the process in which participants, data analysts, and data collectors are kept unaware of the experiment or study. The objective is to limit biased interpretation of treatment. Binding is mainly carried out in an RCT (randomized controlled trial).

It is usually used in clinical research to keep the patients in the dark as to whether they might require a placebo or not. Placebo is something that might appear to be an active treatment to the participants but does not have any active treatment. A control group is a trial condition not receiving the actual treatment and might serve as a baseline.

Many a time, the name of the treatment is also kept hidden. For instance, patients might have an idea that they are involved in some trials for osteoporosis, but they will have zero information about the name of the brand in the trial.

Why is Blinding Important in Research?

Researchers and data analysts performing specific experiments often expect a particular outcome from it, and intentionally or otherwise, evaluate data in a way that goes well with the preferred hypothesis . It is most significant in subjective trials where skewed outcomes are avoided with blinding.

For instance, blinding can be used in an experiment where pain relief is assessed. If the participants here are aware that they are getting a drug, they will most likely report pain relief than those getting a placebo.

Some benefits associated with blinding in research are:

Enhances the validity of results in a trial
Ensures unbiased ascertainment of outcomes
Makes better the reliability of research

Can you think of another benefit?

Different Types of Blinding

There are three types of blinding:

Single Blinded Trial
Double-Blinded Trial
Triple Blinded Trial

1. Single Blinded Trial:

In a single-blinded trial, blinding or masking of any one group is ensured. Usually, the participant is blinded in a single-blinded trial as they are the ones receiving treatment.

Say you conduct an experiment where you compare two types of butter from two different brands, one with low fats and the other with high fats. You chose a total of some 100 participants and told them to taste any one brand of butter. As soon as they taste, they go through an online survey. Now you, as the researcher, know which brand of butter contains how many calories and fats, but the participants do not. This is an example of a single-blinded trial where only an individual or group of individuals know about the experiment. If this one is clear, let us move on to the next type of blinding.

2. Double-Blinded Trial:

In this type of trial, neither the participant knows about the treatment group they are assigned to nor are the researchers interacting with them.

You want to find out whether females consuming high levels of caffeine are more energetic than the rest. What you do here is randomly assign a few women to take a placebo pill and others to go with a caffeine pill. Both the pills are coded and randomly numbered. You tell all the women in the room that they are given a caffeine pill so that you can observe their interaction level in terms of energy.

We call this a double-blinded trial because neither you know who is in which group and nor do the participants. Once you are done collecting the sample, you can check the codes on the pills to assess the final data. It is imperative here that the researchers are also unaware of the complete information, which can later help them calculate results without being biased.

3. Triple Blinded Trial:

A triple-blinded trial is where neither the person governing the treatment nor the subject or the person measuring or collecting data is told about the treatment. So, here three parties are blinded:

The participant
The researcher
The data collector

So, is there someone who knows about the details?

Of course, otherwise, it would not be possible to get the experiment done. In a triple-blinded trial, the principal investigator of the research is not blinded and knows about the treatment, participants, and everything about the trial.

You have just developed a new vaccine, say the COVID-19 vaccine, and want to test its effectiveness on patients on Coronavirus. Now you make two groups; the first group is given a fake vaccine while the other is given the actual one. You have no idea what is happening as a researcher and also have not told the data collectors and participants about the whole deal. This is a triple-blinded trial where the third party does not also have any clue about the experiment.

When is Blinding Not Possible?

We have discussed how effective it is to use single, double, and triple blinding for different treatments and experiments. But it is not always possible.

In many medical trials, if you plan to fake treatment or use a placebo, the entire thing can go wrong. The treatment sometimes cannot be disguised from either the experimenter or the participant or both. For instance, all the treatments that are physical and can only be performed by physical therapists need to be accurate and mindful.

FAQs About Bliding

What does blinding in research mean.

Blinding in research, especially in clinical trials, is the process of concealing details about treatment to one or more individuals directly or indirectly associated with the experiment.

What is the difference between single and double-blinding?

In a single-blinded trial, only the participants are unaware of the treatment, while in the latter, both the experimenters and the participants are blinded.

Why is blinding important?

Blinding helps in enhancing the accuracy of an experiment or study. This means the outcomes are less likely to be influenced by various factors not connected to the tested intervention.

What is a triple-blinded trial?

This is where three of the major parties that are the participants, the data collectors , and the analysts or researchers have zero knowledge about the experiment.

When is blinding not a good idea to consider for a trial?

It is not wise in cases where exposure can only come by interviewing every participant individually. If you apply to the blind in such a situation, the results would be inaccurate, incomplete, and sometimes ruin the whole experiment.

Double-Blind Studies in Research

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

A double-blind study is one in which neither the participants nor the experimenters know who is receiving a particular treatment. This procedure is utilized to prevent bias in research results. Double-blind studies are particularly useful for preventing bias due to demand characteristics or the placebo effect .

For example, let's imagine that researchers are investigating the effects of a new drug. In a double-blind study, the researchers who interact with the participants would not know who was receiving the actual drug and who was receiving a placebo.

A Closer Look at Double-Blind Studies

Let’s take a closer look at what we mean by a double-blind study and how this type of procedure works. As mentioned previously, double-blind indicates that the participants and the experimenters are unaware of who is receiving the real treatment. What exactly do we mean by ‘treatment'? In a psychology experiment, the treatment is the level of the independent variable that the experimenters are manipulating.

This can be contrasted with a single-blind study in which the experimenters are aware of which participants are receiving the treatment while the participants remain unaware.

In such studies, researchers may use what is known as a placebo. A placebo is an inert substance, such as a sugar pill, that has no effect on the individual taking it. The placebo pill is given to participants who are randomly assigned to the control group. A control group is a subset of participants who are not exposed to any levels of the independent variable . This group serves as a baseline to determine if exposure to the independent variable had any significant effects.

Those randomly assigned to the experimental group are given the treatment in question. Data collected from both groups are then compared to determine if the treatment had some impact on the dependent variable .

All participants in the study will take a pill, but only some of them will receive the real drug under investigation. The rest of the subjects will receive an inactive placebo. With a double-blind study, the participants and the experimenters have no idea who is receiving the real drug and who is receiving the sugar pill.

Double-blind experiments are simply not possible in some scenarios. For example, in an experiment looking at which type of psychotherapy is the most effective, it would be impossible to keep participants in the dark about whether or not they actually received therapy.

Reasons to Use a Double-Blind Study

So why would researchers opt for such a procedure? There are a couple of important reasons.

First, since the participants do not know which group they are in, their beliefs about the treatment are less likely to influence the outcome.
Second, since researchers are unaware of which subjects are receiving the real treatment, they are less likely to accidentally reveal subtle clues that might influence the outcome of the research.

The double-blind procedure helps minimize the possible effects of experimenter bias. Such biases often involve the researchers unknowingly influencing the results during the administration or data collection stages of the experiment. Researchers sometimes have subjective feelings and biases that might have an influence on how the subjects respond or how the data is collected.

In one research article, randomized double-blind placebo studies were identified as the "gold standard" when it comes to intervention-based studies. One of the reasons for this is the fact that random assignment reduces the influence of confounding variables.

Imagine that researchers want to determine if consuming energy bars before a demanding athletic event leads to an improvement in performance. The researchers might begin by forming a pool of participants that are fairly equivalent regarding athletic ability. Some participants are randomly assigned to a control group while others are randomly assigned to the experimental group.

Participants are then be asked to eat an energy bar. All of the bars are packaged the same, but some are sports bars while others are simply bar-shaped brownies. The real energy bars contain high levels of protein and vitamins, while the placebo bars do not.

Because this is a double-blind study, neither the participants nor the experimenters know who is consuming the real energy bars and who is consuming the placebo bars.

The participants then complete a predetermined athletic task, and researchers collect data performance. Once all the data has been obtained, researchers can then compare the results of each group and determine if the independent variable had any impact on the dependent variable.

A Word From Verywell

A double-blind study can be a useful research tool in psychology and other scientific areas. By keeping both the experimenters and the participants blind, bias is less likely to influence the results of the experiment.

A double-blind experiment can be set up when the lead experimenter sets up the study but then has a colleague (such as a graduate student) collect the data from participants. The type of study that researchers decide to use, however, may depend upon a variety of factors, including characteristics of the situation, the participants, and the nature of the hypothesis under examination.

National Institutes of Health. FAQs About Clinical Studies .

Misra S. Randomized double blind placebo control studies, the "Gold Standard" in intervention based studies . Indian J Sex Transm Dis AIDS . 2012;33(2):131-4. doi:10.4103/2589-0557.102130

Goodwin, CJ. Research In Psychology: Methods and Design . New York: John Wiley & Sons; 2010.

Kalat, JW. Introduction to Psychology . Boston, MA: Cengage Learning; 2017.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Public Health Notes

Your partner for better health, what is ‘blinding’ in research what are its types.

May 25, 2020 Kusum Wagle Epidemiology 0

Table of Contents

What is Blinding in Research?

Blinding, in research, mentions to a practice where the study population or the stakeholders involved in research are not permitted from knowing certain information or treatment, which may somehow influence the study findings.
Blinding refers to covering up of certain information from one or more research participants in a clinical research study, most commonly a Randomized Controlled Trial (RCT) to reduce bias.
Blinding is mainly carried out while conducting RCTs.

Use of Blinding in Research:

Minimizes bias and maximizes validity of the results.
Improves the reliability of clinical research results.
Prevents conscious or unconscious bias in the design and during execution of a clinical trial.
Allows investigators to control the exposure levels as needed.
Gives an honest research findings.
Allows a realistic statistical comparison.
Helps to ensure unbiased ascertainment of outcomes.

Different Types of Blinding:

There are basically three different types of blinding used in researches:

1. Single blinding or single-masked:

In single blinding, only a single stakeholder i.e. either the participant or the investigator is not informed of the nature of treatment the participant is receiving.
A trial is called single-blind if only one party is blinded . Usually, the participant is blinded and is unaware of the treatment they receive.

2. Double-blinding or double-masked:

Double-blinded study is defined as a study, in which both study population/participant and data collectors/investigators/researchers are not aware of the kind or nature of the treatment given and who receive the treatment.
If both ‘the participants’ and ‘the study staffs’ are blinded , it is known as a double- blind study.

3. Triple blinding:

A clinical trial or experiment in which neither the subject nor the person governing treatment nor an individual measuring the response to the treatment is aware of the particular treatment received by the subject is known as triple blind. Triple blinded studies also lengthen blinding to the data specialists.
In triple blinding, the study participant, the data investigator or data collector and the data analyzer- all are blinded.
Only the Principle Investigator of the research might know about the trial – may it be treatment, drugs or so on.

4. Unblinded or open-label:

It is the exact opposite of blinding, where all the participant, clinicians, data collectors, specialists are well known about the treatment/intervention they receive.

Advantages & Limitations of Different Types of Blinding:


		who are not informed or known about the treatment they are receiving.
	in research.	to complete study by applying double blinding
	.

Limitations of Blinding:

More costly in time and money.
Standardized interventions may be different from common practice.
Many research questions are not suitable for blinding.
Only applicable for some research questions.

References and For More Information:

https://www.medicinenet.com/parkinsons_disease_clinical_trials/article.htm

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2947122/

https://www.iwh.on.ca/what-researchers-mean-by/blinding

Skin – Half Width

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3181999/

16 Advantages and Disadvantages of a Double-Blind Study

http://i-base.info/ttfa/8-clinical-trials-and-research/8-7-randomised-double-blind-placebo-controlled-trials/

https://www.sciencedaily.com/terms/double_blind.htm

https://jamanetwork.com/journals/jama/article-abstract/187750

https://medical-dictionary.thefreedictionary.com/triple+blind

https://www.exordo.com/blog/single-blind-peer-review/

https://onlinelibrary.wiley.com/doi/full/10.1111/j.1538-7836.2008.02848.x

https://methods.sagepub.com/Reference//encyc-of-research-design/n471.xml

advantages of blinding
advantages of blinding in research
advantages of double blinding in research
advantages of single blinding in research
advantages of triple blinding in research
benefits of blinding
double blinding in research
importance of blinding
limitations of blinding
limitations of blinding in research
limitations of double blinding in research
limitations of single blinding in research
limitations of triple blinding in research
necessity of blinding in research
single blinding in research
triple blinding in research
types of blinding in research
what are the types of blinding
what is blinding
what is blinding in research

Frequently asked questions

Why is blinding important in research.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

If there is no sampling frame available (e.g., people with a rare disease)
If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

Reproducing research entails reanalyzing the existing data in the same manner.
Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data .
A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity , because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

Convergent validity : The extent to which your measure corresponds to measures of related constructs
Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

Response variables (they respond to a change in another variable)
Outcome variables (they represent the outcome you want to measure)
Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

Explanatory variables (they explain an event or outcome)
Predictor variables (they can be used to predict the value of a dependent variable)
Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

Open-ended and flexible
Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
Unambiguous, getting straight to the point while still stimulating discussion
Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:

You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

Structured interviews : The questions are predetermined in both topic and order.
Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
Unstructured interviews : None of the questions are predetermined.
Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
Statistical generalization: You use specific numbers about samples to make statements about populations.
Causal reasoning: You make cause-and-effect links between different things.
Sign reasoning: You make a conclusion about a correlational relationship between different things.
Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

Reduce research bias that comes from using a single method, theory, or investigator
Enhance validity by approaching the same topic with different tools
Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

Data triangulation : Using data from different times, spaces, and people
Investigator triangulation : Involving multiple researchers in collecting or analyzing data
Theory triangulation : Using varying theoretical perspectives in your research
Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps:

First, the author submits the manuscript to the editor.
Reject the manuscript and send it back to author, or
Send it onward to the selected peer reviewer(s)
Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions.
Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

Both variables are on an interval or ratio level of measurement
Data from both variables follow normal distributions
Your data have no outliers
Your data is from a random or representative sample
You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

Your research questions and/or hypotheses
Your overall approach (e.g., qualitative or quantitative )
The type of design you’re using (e.g., a survey , experiment , or case study )
Your sampling methods or criteria for selecting subjects
Your data collection methods (e.g., questionnaires , observations)
Your data collection procedures (e.g., operationalization , timing and data management)
Your data analysis methods (e.g., statistical tests or thematic analysis )

A research design is a strategy for answering your research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

A positive correlation means that both variables change in the same direction.
A negative correlation means that the variables change in opposite directions.
A zero correlation means there’s no relationship between the variables.

Random error is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

If you have quantitative variables , use a scatterplot or a line graph.
If your response variable is categorical, use a scatterplot or a line graph.
If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

An explanatory variable is the expected cause, and it explains the results.
A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

A control group that receives a standard treatment, a fake treatment, or no treatment.
Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
Experimenter effects : unintentional actions by researchers that influence study outcomes.
Situational variables : environmental variables that alter participants’ behaviors.
Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

Only requires small samples
Statistically powerful
Removes the effects of individual differences on the outcomes

Disadvantages:

Internal validity threats reduce the likelihood of establishing a direct relationship between variables
Time-related effects, such as growth, can influence the outcomes
Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

Prevents carryover effects of learning and fatigue.
Shorter study duration.
Needs larger samples for high power.
Uses more resources to recruit participants, administer sessions, cover costs, etc.
Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

It’s caused by the independent variable .
It influences the dependent variable
When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

In single-stage sampling , you collect data from every unit within the selected clusters.
In double-stage sampling , you select a random sample of units from within the clusters.
In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

In a single-blind study , only the participants are blinded.
In a double-blind study , both participants and experimenters are blinded.
In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

Prepare and organize your data.
Review and explore your data.
Develop a data coding system.
Assign codes to the data.
Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

Grounded theory involves collecting data in order to develop new theories.
Ethnography involves immersing yourself in a group or organization to understand its culture.
Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
Phenomenological research involves investigating phenomena through people’s lived experiences.
Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

The type of soda – diet or regular – is the independent variable .
The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal study	Cross-sectional study
observations	Observations at a in time
Observes the multiple times	Observes (a “cross-section”) in the population
Follows in participants over time	Provides of society at a given point

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

Discrete variables represent counts (e.g. the number of objects in a collection).
Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

The independent variable is the amount of nutrients added to the crop field.
The dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

A testable hypothesis
At least one independent variable that can be precisely manipulated
At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

How you will manipulate the variable(s)
How you will control for any potential confounding variables
How many subjects or samples will be included in the study
How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Double Blind Studies in Research: Types, Pros & Cons

In the medical field, it is unethical to not inform your patient of a process or a procedure you want to carry out on them. It is required that the patients are informed about the treatment they would be given and that they consent to it.

However, there is a method known as the blind study in psychological research. A blind study prevents the participants from knowing about their treatment to avoid bias in the research.

This article will focus on the double-blind study which is a type of blind study which leaves both the researcher and the participants in the dark about important details of the study . That way the research is expected to be bias-free and far from any external influence.

The blind study has no ground in patient-doctor physical therapy sessions, but it is very helpful in other studies such as pharmacological research.

This is why we will consider a double-blind study, its usefulness, advantages, and disadvantages in a study or research.

What is a Blinded Study?

A blinded study is research conducted in a way that prevents the subjects ( blind the subjects) from knowing the treatment they are given so that the researcher is guaranteed a biased free result. Information that can influence the subjects of a research is withheld from the subjects until the completion of the research.

If good blinding is carried out on the subjects, it can eliminate any form of biases that may arise from the subjects’ expectations, influence from the researcher, researcher’s bias , and other forms of biases that may occur in a research test.

This can be achieved as a blind study can be imposed on all participants in research. From the researcher to the subjects, the analysts, and even the judges or evaluators.

Free to use: Participant Consent Form Template

In some cases, however, imposing blind study in research may be impossible or even unethical. For example, it is unethical for a medical practitioner to blind a patient from knowing their treatment. The ethical thing to do is let your patient be informed about a major part of their treatment if it’s in a face-to-face intervention.

A subject can become unblinded during a study if they obtain information that has been previously shielded away from them. For example, if due to experiencing some side effects symptoms, a subject could correctly guess the treatment he/she has been exposed to. The subject then becomes unblinded. Subjects becoming unblinded mostly occur in pharmacological testings.

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What is a Double-blind Study?

Double-blind refers to a study or research where both the subjects or participants of a study and the researchers are oblivious of the treatment being given and the subjects receiving the treatment. Both the participants and the experimenter are kept in the dark. This is done to eliminate all presence of biases in the outcome of the research.

It is most useful in research because of the placebo effect.

For example, if a researcher wants to conduct research on the effects of a newly introduced drug . A double-blind study requires that both the researcher and the subjects are unaware of the process.

So the researcher that is analyzing the subjects would have no information about the subjects receiving the new drug (which is the treatment group) and those who are not receiving the drug (which is the control group).

Now if the participants are not aware of their treatment and the researcher is not provided with information on who is receiving the treatment, the question that requires an answer is, why is a double-blind study needed?

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Purpose of a Double-blinded Study

Every procedure has its purpose and a double-blind study is not left out. The purpose of a double-blind study is to make sure that the outcomes of a study are free from biases. Using the double-blind method in a study improves the level of credibility and validity of the study

A double-blind study is used in the scientific field, psychologists, and also in the legal process.

Read more: What are Cross-Sectional Studies: Examples, Definition, Types

Types of Blinded Studies

There are three types of blind studies namely single-blind study, double-blind study, and triple-blind study

1. Single-blind study : in this type of blind study only the subjects in the experiment are prevented from knowing the treatment they are given. The single-blind study is also known as the single masked study.

2. Double-blind study: In the double-blind study both the subjects or participants and the researcher are blinded. The researcher is unaware of who is receiving what treatment and the participants are unaware of the treatment they are receiving.

3. Triple blind study : here in the triple-blind study the participants, the researcher analyzing data , and the data collector are blinded from the information about the study. These three groups are prevented from knowing the treatments being given out or being received.

When to Use Each Type of Blind Study

Now that we know the types of blind study we are going to consider when it is appropriate to use either of these types of blind study in research.

When to use a single-blind study

A single-blind study is usually conducted to prevent the subject from being aware of the treatment being studied. This is in case they get influenced and that leads to bias in the outcome.

It should be noted that there are cases where blinding a participant or patient is considered unethical. Therefore, single-blind study should only be used in statistical research or studies that don’t involve physical therapy between a patient and a doctor.

When to use a double-blind study

Double-blind study is conducted when both the participants and the researcher are not allowed to know details of the research. This process is used to prevent bias in the study results and when there is a need to understand the characteristics of the results or to understand placebo effect.

When to use a triple-blind study :

Use triple blind study if you aim to reduce your study and improve the accuracy of your results. This is because a triple-blind study allows randomization where the treatment item and the intervention are not known to the participants, researcher, data collector , or clinical personnel.

Read: Survey Errors To Avoid: Types, Sources, Examples, Mitigation

Advantages of Double-blinded Study

The following are the advantages of double-blind study:

1. It tests for three groups

The double-blind study usually involves three groups of subjects. The first is the treatment group, then the placebo, and lastly, the control group. The treatment group and the placebo are given the test item even though the researcher wouldn’t know which group is getting the treatment. No test item is administered to the control group because they are used as a basis of comparison for the results of the treatment group and the placebo.

If there’s a significant improvement in the placebo group over the control group, then it is considered that the treatment administered worked.

2. Reduces experimental bias

A double-blind study reduces the risk of biases in research. Biases can occur when a researcher influences the outcome of a study directly or otherwise. However, because the researcher is often also in the dark, it is difficult to influence the study.

This allows for credible, reliable, and valid research results.

3. Result duplication

The results of a double-blind study can be duplicated and that is why this procedure is considered one of the best practices. A double-blind study allows other researchers to follow up with the same processes, apply the test item, and compare the result with the control group.

The usefulness of this method is that if the results from these studies are close, it proves the validity of the test item that was administered. If there is no duplication in the research results, another study has to be carried out to determine why.

Disadvantages of a Double-Blind Study

1. it is expensive.

One huge disadvantage of a double-blind study is that it is expensive to conduct. It takes several months or years to complete because the researcher has to examine all the possible variables and they may have to use different groups to gather enough data.

Many corporations after estimating the cost of this study which runs into millions of Dollars might have to spread the research across multiple months. Even for government studies, conducting this study may run into billions of dollars thereby making the medicine expensive in the market. This is one of the reasons why new prescription medicines are sold at an expensive price in the market.

2. Low representation

A double-blind study cannot provide a properly represented sample group because it is small. Most double-blind study is designed to enroll at least 100 people or participants for the research however the most preferable number is 300. It is true that the effectiveness of a treatment can be proven even in small studies but more people or participants are required to determine a pattern in research so that the results can be properly analyzed and verified.

Research generally requires participants in large numbers to participate in the trials and progress of a treatment being administered or in plan to be introduced to the market.

This is because even when the product or treatment item has gotten to the third phase of testing it still has only a 60% chance to proceed to another stage.

3. Negative reaction

In some cases some of the participants may react negatively to the treatment item when this happens the results from the test can be compared to see what changed. Some participants may react negatively to the placebo which may lead to producing some side effects that may make it seem like they were receiving the treatment item when they did not.

4. Time factor

Many times it is almost impossible to complete a double-blind study. For example, you cannot keep the subject or participants of a psychotherapy experiment in the dark about who gets the treatment item and who doesn’t get the treatment item. Double-blind study can only work in this scenario if you find a way to provide two similar procedures without each of the groups communicating about which group is getting the treatment item and which group is getting the placebo.

Frequently Asked Questions about Blind Studies

Which is better: single-blind or double-blind study?

To determine which is best between a single-blind study and a double-blind study the case being studied has to be considered.

For example, if a researcher is conducting a study on the effects of a medicine that can cure Alzheimer’s, it is best to use a double-blind study rather than a single-blind study. This is because the participants will be unaware if they received the treatment item from the real drug or if they received the placebo which in turn reduces any external influence on the results of the test.

When would you use a single-blind study?

Use a single-blind study if the participants having knowledge of the group they belong to might result in bias. I.e. whether their being aware of the treatment item and the questions of the study might result in bias.

What is the difference between a single and double-blind study?

The significant difference between a single study and a double-blind study is that in a single-blind study only the participants or the patient are blinded while in a double-blind study both the participant and the researcher are blinded.

In any study, it is good to know how the results of the treatment group and the response group compare in an experiment. This is why a double-blind study is important.

The risk of anyone manipulating data or influencing the participants is averted since a double-blind study prevents both the researcher or the participants from obtaining in-depth knowledge of the study.

You can be assured that the researcher cannot accidentally communicate with the subjects or participants. Now that is one huge importance and psychological benefit of the placebo effect.

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Introduction
Conclusions
Article Information

a Includes 10 individuals for whom the site enrollment closed and 1 who was lost to follow-up.

b See Table 3 and eTable 4 in Supplement 2 for the details of the adverse events that led to treatment discontinuation.

c The most common reasons for participant withdrawal included participant no longer wished to participate, participant unavailable to attend visits, participant moved out of state or country, and personal or family issues.

d Guided by the treatment regimen estimand.

Observed mean values from the full analysis set are shown. Error bars represent 95% CI for the mean. The dashed vertical line at week 36 represents the randomization point. Analysis of covariance using the full analysis set with hybrid imputation least-square mean values at week 88 is also shown on the right. See eTable 3 in Supplement 2 for corresponding data for the efficacy estimand.

Trial protocol

eAppendix 1. Eligibility criteria

eAppendix 2. Concomitant medications

eAppendix 3. Statistical analyses

eTable 1. Additional demographics and clinical characteristics (randomized population)

eTable 2. Changes during the tirzepatide lead-in treatment period (randomized population)

eTable 3. Primary and secondary end points (efficacy estimand)

eTable 4. Adverse events during the tirzepatide lead-in treatment period (week 0 to 36)

eTable 5. COVID-19-related adverse events

eTable 6. Reported deaths during the entire study

eTable 7. Additional safety measures during the double-blind period (safety analysis set)

eTable 8. Vital signs abnormalities during the double-blind period (safety analysis set)

eFigure 1. SURMOUNT-4 study design

eFigure 2. Effect of tirzepatide maximum tolerated dose (10 or 15 mg) compared with placebo on efficacy outcomes in the SURMOUNT-4 trial

eFigure 3. Cumulative distribution plot of the percent change in weight (efficacy estimand)

eFigure 4. Time, during the 52-week double-blind period (week 36 to 88 in the entire study), to first occurrence of participant returning to >95% baseline body weight if already lost ≥5% since week 0

eFigure 5. Box plot of the percent change in body weight over time during the entire study

eFigure 6. Waterfall plot of the percent change in body weight from week 0 to 88

eFigure 7. Body weight over time during the entire study

eFigure 8. Change in blood pressure over time during the entire study

eFigure 9. Incidence of nausea, vomiting, and diarrhea over time during the tirzepatide lead-in treatment period

eFigure 10. Incidence of nausea, vomiting, and diarrhea over time during the double-blind period (safety analysis set)

Statistical analysis

Nonauthor collaborators

Data sharing statement

Tirzepatide vs Insulin Lispro Added to Basal Insulin in Type 2 Diabetes JAMA Original Investigation November 7, 2023 This study examines the efficacy and safety of tirzepatide vs insulin lispro adjunctive therapy to insulin glargine among those receiving basal insulin with inadequately controlled type 2 diabetes. Julio Rosenstock, MD; Juan P. Frías, MD; Helena W. Rodbard, MD; Santiago Tofé, MD; Emmalee Sears, MS; Ruth Huh, PhD; Laura Fernández Landó, MD; Hiren Patel, MPharm
What to Know About Zepbound, the Newest Antiobesity Drug JAMA Medical News & Perspectives December 12, 2023 This Medical News article discusses the November 8 US Food and Drug Administration approval of the drug tirzepatide for chronic weight management in people with obesity or overweight with weight-related conditions. Jennifer Abbasi
Direct-to-Consumer Drug Company Pharmacies JAMA Viewpoint March 26, 2024 The recent launch of direct-to-consumer pharmacy LillyDirect prompts the author of this Viewpoint to consider why it was created and also to raise concerns about allowing manufacturers to sell their drugs directly to patients. Benjamin N. Rome, MD, MPH
Tirzepatide for Maintenance of Weight Reduction in Adults With Obesity—Reply JAMA Comment & Response May 21, 2024 Louis J. Aronne, MD; SURMOUNT-4 Investigators
Tirzepatide for Maintenance of Weight Reduction in Adults with Obesity JAMA Comment & Response May 21, 2024 Yifan Xiao, BS; Jiahao Meng, BS; Shuguang Gao, MD
Effect of Tirzepatide in Chinese Adults With Obesity JAMA Original Investigation May 31, 2024 This randomized clinical trial investigates the safety and efficacy of treatment with once-weekly tirzepatide for weight reduction in Chinese adults with overweight or obesity without diabetes over a 52-week period. Lin Zhao, MD; Zhifeng Cheng, MD; Yibing Lu, MD; Ming Liu, MD; Hong Chen, MD; Min Zhang, MD; Rui Wang, MD; Yuan Yuan, PhD; Xiaoying Li, MD

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Aronne LJ , Sattar N , Horn DB, et al. Continued Treatment With Tirzepatide for Maintenance of Weight Reduction in Adults With Obesity : The SURMOUNT-4 Randomized Clinical Trial . JAMA. 2024;331(1):38–48. doi:10.1001/jama.2023.24945

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Continued Treatment With Tirzepatide for Maintenance of Weight Reduction in Adults With Obesity : The SURMOUNT-4 Randomized Clinical Trial

1 Comprehensive Weight Control Center, Division of Endocrinology, Diabetes, and Metabolism, Weill Cornell Medicine, New York, New York
2 BHF Glasgow Cardiovascular Research Centre, School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
3 University of Texas Center for Obesity Medicine and Metabolic Performance, Department of Surgery, University of Texas McGovern Medical School, Houston
4 Louisville Metabolic and Atherosclerosis Research Center, Louisville, Kentucky
5 McMaster University, and Wharton Weight Management Clinic, York University, Toronto, Ontario, Canada
6 Department of Family Medicine, China Medical University Hospital, Taichung, Taiwan
7 Eli Lilly and Company, Indianapolis, Indiana
8 Eli Lilly and Company, Moscow, Russia
Original Investigation Tirzepatide vs Insulin Lispro Added to Basal Insulin in Type 2 Diabetes Julio Rosenstock, MD; Juan P. Frías, MD; Helena W. Rodbard, MD; Santiago Tofé, MD; Emmalee Sears, MS; Ruth Huh, PhD; Laura Fernández Landó, MD; Hiren Patel, MPharm JAMA
Medical News & Perspectives What to Know About Zepbound, the Newest Antiobesity Drug Jennifer Abbasi JAMA
Viewpoint Direct-to-Consumer Drug Company Pharmacies Benjamin N. Rome, MD, MPH JAMA
Comment & Response Tirzepatide for Maintenance of Weight Reduction in Adults With Obesity—Reply Louis J. Aronne, MD; SURMOUNT-4 Investigators JAMA
Comment & Response Tirzepatide for Maintenance of Weight Reduction in Adults with Obesity Yifan Xiao, BS; Jiahao Meng, BS; Shuguang Gao, MD JAMA
Original Investigation Effect of Tirzepatide in Chinese Adults With Obesity Lin Zhao, MD; Zhifeng Cheng, MD; Yibing Lu, MD; Ming Liu, MD; Hong Chen, MD; Min Zhang, MD; Rui Wang, MD; Yuan Yuan, PhD; Xiaoying Li, MD JAMA

Question Does once-weekly subcutaneous tirzepatide with diet and physical activity affect maintenance of body weight reduction in individuals with obesity or overweight?

Findings After 36 weeks of open-label maximum tolerated dose of tirzepatide (10 or 15 mg), adults (n = 670) with obesity or overweight (without diabetes) experienced a mean weight reduction of 20.9%. From randomization (at week 36), those switched to placebo experienced a 14% weight regain and those continuing tirzepatide experienced an additional 5.5% weight reduction during the 52-week double-blind period.

Meaning In participants with obesity/overweight, withdrawing tirzepatide led to substantial regain of lost weight, whereas continued treatment maintained and augmented initial weight reduction.

Importance The effect of continued treatment with tirzepatide on maintaining initial weight reduction is unknown.

Objective To assess the effect of tirzepatide, with diet and physical activity, on the maintenance of weight reduction.

Design, Setting, and Participants This phase 3, randomized withdrawal clinical trial conducted at 70 sites in 4 countries with a 36-week, open-label tirzepatide lead-in period followed by a 52-week, double-blind, placebo-controlled period included adults with a body mass index greater than or equal to 30 or greater than or equal to 27 and a weight-related complication, excluding diabetes.

Interventions Participants (n = 783) enrolled in an open-label lead-in period received once-weekly subcutaneous maximum tolerated dose (10 or 15 mg) of tirzepatide for 36 weeks. At week 36, a total of 670 participants were randomized (1:1) to continue receiving tirzepatide (n = 335) or switch to placebo (n = 335) for 52 weeks.

Main Outcomes and Measures The primary end point was the mean percent change in weight from week 36 (randomization) to week 88. Key secondary end points included the proportion of participants at week 88 who maintained at least 80% of the weight loss during the lead-in period.

Results Participants (n = 670; mean age, 48 years; 473 [71%] women; mean weight, 107.3 kg) who completed the 36-week lead-in period experienced a mean weight reduction of 20.9%. The mean percent weight change from week 36 to week 88 was −5.5% with tirzepatide vs 14.0% with placebo (difference, −19.4% [95% CI, −21.2% to −17.7%]; P < .001). Overall, 300 participants (89.5%) receiving tirzepatide at 88 weeks maintained at least 80% of the weight loss during the lead-in period compared with 16.6% receiving placebo ( P < .001). The overall mean weight reduction from week 0 to 88 was 25.3% for tirzepatide and 9.9% for placebo. The most common adverse events were mostly mild to moderate gastrointestinal events, which occurred more commonly with tirzepatide vs placebo.

Conclusions and Relevance In participants with obesity or overweight, withdrawing tirzepatide led to substantial regain of lost weight, whereas continued treatment maintained and augmented initial weight reduction.

Trial Registration ClinicalTrials.gov Identifier: NCT04660643

Obesity is a serious chronic, progressive, and relapsing disease. 1 Lifestyle interventions are a cornerstone of obesity management; however, sustaining weight reduction achieved through lifestyle-based caloric restriction is challenging.

Therefore, current guidelines recommend adjunctive antiobesity medications to promote weight reduction, facilitate weight maintenance, and improve health outcomes in people with obesity. 2 - 4 Randomized withdrawal studies of antiobesity medications to date have consistently demonstrated clinically significant body weight regain with cessation of therapy. 5 , 6 There is also evidence that antiobesity medications, including long-acting glucagon-like peptide-1 (GLP-1) receptor agonists, naltrexone/bupropion, phentermine/topiramate, and orlistat, may help maintenance of achieved weight reduction. 5 , 7 - 12

Tirzepatide is a single molecule that combines glucose-dependent insulinotropic polypeptide (GIP) and GLP-1 receptor agonism 13 resulting in synergistic effects on appetite, food intake, and metabolic function. 14 - 16 Tirzepatide is approved in many countries, including the US, EU, and Japan, as a once-weekly subcutaneous injectable for type 2 diabetes and for the treatment of obesity in the US and UK. 16 - 18 In a placebo-controlled trial of participants with obesity or overweight without diabetes, tirzepatide led to mean reductions in body weight up to 20.9% after 72 weeks of treatment. 17 , 18

The aim of the SURMOUNT-4 trial was to investigate the effect of continued treatment with the maximum tolerated dose (ie, 10 or 15 mg) of once-weekly tirzepatide, compared with placebo, on the maintenance of weight reduction following an initial open-label lead-in treatment period in participants with obesity or overweight.

SURMOUNT-4 was a phase 3 randomized withdrawal study with a 36-week, open-label tirzepatide lead-in period followed by a 52-week, double-blind, placebo-controlled period conducted at 70 sites in Argentina, Brazil, Taiwan, and the US. The trial started on March 29, 2021, and finished on May 18, 2023. The study protocol ( Supplement 1 ) was approved by the ethical review board at each site and was followed according to local regulations and the principles of the Declaration of Helsinki, Council of International Organizations of Medical Sciences International Ethical Guidelines, and Good Clinical Practice guidelines. Written informed consent was obtained from all participants before participation in this study.

Eligible participants (18 years or older) had a body mass index (BMI) greater than or equal to 30 or greater than or equal to 27 and at least 1 weight-related complication (ie, hypertension, dyslipidemia, obstructive sleep apnea, or cardiovascular disease). Key exclusion criteria included diabetes, prior or planned surgical treatment for obesity, and treatment with a medication that promotes weight loss within 3 months prior to enrollment. Full eligibility criteria are shown in eAppendix 1 in Supplement 2 . The study was not designed to represent the racial diversity of each of the participating countries. Race and ethnicity were self-reported by participants in this study using fixed selection categories.

Tirzepatide was administered once weekly as a subcutaneous injection. During the 36-week, open-label lead-in period, the starting dose of tirzepatide was 2.5 mg and was increased by 2.5 mg every 4 weeks until a maximum tolerated dose of 10 or 15 mg was achieved (eFigure 1 in Supplement 2 ). Throughout the study, gastrointestinal symptoms were managed by dietary counseling, symptomatic medications per the investigator’s discretion, or skipping of a single dose of treatment as described in the protocol ( Supplement 1 ). During the lead-in period, if these mitigations were not successful, a cycle of tirzepatide dose deescalation and reescalation (in 2.5-mg increments) was allowed. At the end of the lead-in period, participants who attained the maximum tolerated dose of tirzepatide (10 or 15 mg) were randomized in a 1:1 ratio by a computer-generated random sequence using an interactive web-response system to either continue receiving the maximum tolerated dose of tirzepatide or switch to matching placebo for an additional 52 weeks. Randomization was stratified by country, sex, maximum tolerated dose of tirzepatide, and percent weight reduction at week 36 (<10% vs ≥10%). Dose adjustments were not permitted during the double-blind treatment period.

Quiz Ref ID All participants received lifestyle counseling by a qualified health care professional throughout the study to encourage adherence to a healthy 500 kcal/d deficit diet and at least 150 minutes of physical activity per week. The use of concomitant medications is described in eAppendix 2 in Supplement 2 .

The primary end point was the percent change in body weight from randomization (week 36) to week 88. Key secondary end points capturing weight maintenance and regain, respectively, were the proportion of participants at week 88 maintaining at least 80% of the body weight loss during the 36-week open-label period and time during the 52-week double-blind treatment period to first occurrence of participants returning to greater than 95% baseline body weight for those who lost at least 5% during the open-label lead-in period. Key secondary end points also included change in absolute body weight and waist circumference during the double-blind period (week 36 to 88) and the proportion of participants achieving weight reduction thresholds of at least 5%, at least 10%, at least 15%, and at least 20% since enrollment (week 0 to 88); the proportion of participants achieving at least 25% weight reduction from week 0 to 88 was a prespecified exploratory end point.

Additional secondary end points included change from randomization (week 36) to week 88 and from enrollment (week 0) to week 88 in cardiometabolic risk factors including glycemic parameters, fasting insulin, lipids, blood pressure, and patient-reported outcomes measured by the Short Form-36 Version 2 Health Survey (SF-36 v2) acute form and Impact of Weight on Quality of Life-Lite-Clinical Trials Version (IWQOL-Lite-CT).

Safety assessments included treatment-emergent adverse events, serious adverse events, and early discontinuation of study drug due to adverse events during the tirzepatide lead-in treatment period (weeks 0-36), the double-blind period (weeks 36-88), and safety follow-up period. Cases of major adverse cardiovascular events, acute pancreatitis, and deaths were reviewed by an independent external adjudication committee.

A sample size of 600 randomized participants provided greater than 90% power to demonstrate superiority of maximum tolerated dose of tirzepatide vs placebo for the primary end point at a 2-sided significance level of .05 using a 2-sample t test. The calculation assumed a dropout rate of up to 25%, a difference between treatment groups of at least 6% in mean percent change in body weight from randomization (week 36) to week 88, and a common SD of 8% based on data from 2 phase 2 trials. 19 , 20

Unless stated otherwise, efficacy end points were analyzed using the full analysis set (data obtained during the double-blind period, regardless of adherence to study drug) and the efficacy analysis set (data obtained during the double-blind period, excluding data after discontinuation of study drug). Assessment of adverse events and laboratory parameters used the safety analysis set (data obtained during the double-blind period and safety follow-up period, regardless of adherence to study drug). All results from statistical analyses were accompanied by 2-sided 95% CIs and corresponding P values (statistical significance was defined as P < .05). Statistical analyses were performed using SAS version 9.4 (SAS Institute).

Two estimands (treatment regimen estimand and efficacy estimand) were used to assess efficacy from different perspectives and accounted for intercurrent events and missing data. 21 The treatment regimen estimand was conducted on the full analysis set representing the mean treatment effect of tirzepatide relative to placebo for all participants who had undergone randomization, regardless of treatment adherence. If intercurrent events led to missing data, the missingness was assumed to be related to treatment, except for intercurrent events solely due to COVID-19, for which missing at random was assumed. The efficacy estimand was conducted on the efficacy analysis set representing the mean treatment effect of tirzepatide relative to placebo for all participants who had undergone randomization if the treatment was administered as intended (ie, excluding the data collected after study drug discontinuation). Continuous end points were analyzed using an analysis of covariance model for the treatment regimen estimand and a mixed model for repeated measures for the efficacy estimand, and categorical end points were analyzed by logistic regression for both estimands (treatment difference was assessed by odds ratio). Details on statistical analysis methods, estimands, and handling of missing values are provided in eAppendix 3 in Supplement 2 and the statistical analysis plan ( Supplement 3 ). All reported results are for the treatment regimen estimand unless stated otherwise. The type I error rate was controlled within each estimand independently for evaluation of primary and key secondary end points with a graphical approach (eAppendix 3 in Supplement 2 ). Because of the potential for type I error due to multiple comparisons, findings for analyses of additional secondary end points should be interpreted as exploratory.

A total of 952 patients were screened and 783 were enrolled in the 36-week open-label tirzepatide lead-in treatment period. Among enrolled participants, 113 discontinued the study drug during the lead-in period, most commonly due to an adverse event or participant withdrawal ( Figure 1 ). A total of 670 participants (92.7% achieved a maximum tolerated dose of 15 mg and 7.3% achieved a maximum tolerated dose of 10 mg) were randomized to continue receiving the maximum tolerated dose of tirzepatide (n = 335) or switch to receiving placebo (n = 335). Of the randomized participants, 600 (89.6%) completed the study and 575 (85.8%) completed the study while receiving the study drug. Withdrawal and “other” (mainly in the placebo group as lack of efficacy) were the most common reasons for premature study drug discontinuation during the double-blind period ( Figure 1 ).

Most randomized participants were women (70.6%) and White (80.1%), with an overall mean age of 48 years, body weight of 107.3 kg, BMI of 38.4, and waist circumference of 115.2 cm at enrollment (week 0; Table 1 ). The mean duration of obesity was 15.5 years and 69.4% participants had 1 or more weight-related complication (eTable 1 in Supplement 2 ), with hypertension and dyslipidemia being the most prevalent ( Table 1 ). Demographics and clinical characteristics at randomization (week 36) were similar across tirzepatide and placebo groups, with overall mean body weight of 85.2 kg, BMI of 30.5, and waist circumference of 97.5 cm.

During the open-label tirzepatide lead-in period (week 0 to 36), randomized participants achieved a mean weight reduction of 20.9%, with reductions in BMI and waist circumference and improvements in blood pressure, glycemic parameters, lipid levels, and patient-reported outcomes (eTable 2 in Supplement 2 ).

Quiz Ref ID For the treatment regimen estimand, the mean percent change in weight from week 36 to week 88 was −5.5% with tirzepatide vs 14.0% with placebo (difference, −19.4% [95% CI, −21.2% to −17.7%]; P < .001; Table 2 ; eFigure 2A in Supplement 2 ). For the efficacy estimand, corresponding changes were −6.7% with tirzepatide vs 14.8% with placebo (difference, −21.4% [95% CI, −22.9% to −20.0%]; P < .001; eTable 3 and eFigure 3 in Supplement 2 ).

At week 88, a significantly greater percentage of participants who continued receiving tirzepatide vs placebo maintained at least 80% of the body weight loss during the 36-week open-label tirzepatide lead-in treatment period (89.5% vs 16.6%; P < .001; treatment regimen estimand; Table 2 ; eFigure 2B in Supplement 2 ). Consistent results were observed when using the efficacy estimand (eTable 3 in Supplement 2 ). Time-to-event analysis showed that continued tirzepatide treatment during the double-blind period reduced the risk of returning to greater than 95% baseline body weight for those who had already lost at least 5% since week 0 by approximately 98% compared with placebo (hazard ratio, 0.02 [95% CI, 0.01 to 0.06]; P < .001) for the treatment regimen estimand, which was consistent with the results for the efficacy estimand (eFigure 4 in Supplement 2 ). The mean change from week 36 to week 88 in body weight and waist circumference is presented in Table 2 for the treatment regimen estimand and in eTable 3 in Supplement 2 for the efficacy estimand.

Relative to placebo, tirzepatide was associated with significant improvements from randomization at week 36 to week 88 in BMI, hemoglobin A 1c , fasting glucose, insulin, lipid levels, and systolic and diastolic blood pressure ( P < .001 for all except P = .014 for high-density lipoprotein cholesterol and P = .008 for free fatty acids) (eTable 3 in Supplement 2 ; efficacy estimand). Significant improvements were observed in the SF-36 v2 physical functioning, role-physical, role-emotional, and mental health domain scores and IWQOL-Lite-CT physical function composite scores with tirzepatide vs placebo from week 36 to week 88 ( P < .001 for all except P = .015 for SF-36 v2 role-physical score and P = .001 for SF-36 v2 role-emotional score) (eTable 3 in Supplement 2 ; efficacy estimand).

A significantly greater percentage of participants continuing tirzepatide vs placebo met the weight reduction thresholds of at least 5% (97.3% vs 70.3%), at least 10% (92.1% vs 46.2%), at least 15% (84.1% vs 25.9%), and at least 20% (69.5% vs 12.6%) from week 0 to week 88 ( P < .001 for all; treatment regimen estimand; Table 2 ; eFigure 2C in Supplement 2 ). Consistent results were observed when using the efficacy estimand (eTable 3 in Supplement 2 ).

Compared with placebo, tirzepatide was associated with improvements throughout the entire study (from week 0 to week 88) in body weight, BMI, cardiometabolic parameters (waist circumference, hemoglobin A 1c , fasting glucose, insulin, lipid levels, and systolic and diastolic blood pressure), and patient-reported outcomes ( P < .001 for all except P = .004 for free fatty acids and P = .064 for high-density lipoprotein cholesterol) ( Figure 2 and eTable 3, eFigure 3, and eFigure 5-8 in Supplement 2 ).

A greater percentage of participants continuing tirzepatide vs placebo achieved the prespecified exploratory end point of at least 25% weight reduction from week 0 to week 88 with the treatment regimen estimand (54.5% vs 5.0%; P < .001; Table 2 and eFigure 2C in Supplement 2 ) and the efficacy estimand (eTable 3 in Supplement 2 ).

A total of 81.0% of participants reported at least 1 treatment-emergent adverse event during the tirzepatide lead-in treatment period, with the most frequent events being gastrointestinal (nausea [35.5%], diarrhea, [21.1%], constipation [20.7%], and vomiting [16.3%]; eTable 4 in Supplement 2 ). During the double-blind period, 60.3% of participants continuing tirzepatide reported at least 1 treatment-emergent adverse event compared with 55.8% of participants who switched to placebo ( Table 3 ). The most frequent treatment-emergent adverse events during the double-blind period were COVID-19 and gastrointestinal disorders. Gastrointestinal events were more common in the tirzepatide group than in the placebo group (diarrhea, 10.7% vs 4.8%; nausea, 8.1% vs 2.7%; and vomiting, 5.7% vs 1.2%; Table 3 ). Most gastrointestinal events were mild to moderate in severity, and incidence of new events decreased over time in tirzepatide-treated participants during the lead-in period and leveled off during the double-blind period (eFigure 9 and eFigure 10 in Supplement 2 ).

Treatment discontinuation due to an adverse event occurred in 7.0% of enrolled participants during the tirzepatide lead-in treatment period, mainly due to gastrointestinal events (eTable 4 in Supplement 2 ). Quiz Ref ID During the double-blind period, treatment discontinuation due to an adverse event occurred in 1.8% of participants in the tirzepatide group and 0.9% in placebo group ( Table 3 ).

Overall, 16 participants (2.0%) reported serious adverse events during the lead-in period (eTable 4 in Supplement 2 ) and 10 (3.0%) during the double-blind period, with similar percentages across treatment groups ( Table 3 ). There was 1 death reported during the tirzepatide lead-in treatment period due to COVID-19 pneumonia and 2 deaths reported during the double-blind period (1 in the tirzepatide group due to congestive heart failure and 1 in the placebo group due to adenocarcinoma of the colon; eTable 6 in Supplement 2 ). None of the deaths were considered by investigators to be related to the study drug.

There were no adjudication-confirmed cases of pancreatitis reported during the study ( Table 3 ; eTable 4 in Supplement 2 ). Cholelithiasis was reported in 7 participants (0.9%) during the tirzepatide lead-in treatment period (eTable 4 in Supplement 2 ) and in 1 participant (0.3%) in both the tirzepatide group and placebo group during the double-blind period ( Table 3 ). Acute cholecystitis was reported in 4 participants (0.5%) during the tirzepatide lead-in treatment period (eTable 4 in Supplement 2 ) and in 3 (0.9%) in the placebo group during the double-blind period ( Table 3 ). No cases of medullary thyroid carcinoma or pancreatic cancer were reported.

Other adverse events of special interest are described in Table 3 and eTable 4 in Supplement 2 and additional safety variables are described in eTable 7 and eTable 8 in Supplement 2 .

The SURMOUNT-4 trial results emphasize the need to continue pharmacotherapy to prevent weight regain and ensure the maintenance of weight reduction and its associated cardiometabolic benefits. 22 At least 5 trials (including the present study) across various classes of medications, including potent antiobesity medications such as semaglutide, have demonstrated that weight is substantially regained after cessation of pharmacotherapy. 5 , 6 , 23 , 24

The consistency of these data across therapeutic classes spanning more than 2 decades suggests that obesity is a chronic metabolic condition similar to type 2 diabetes and hypertension requiring long-term therapy in most patients.

A notable finding in the SURMOUNT-4 trial is that after switching to placebo for 1 year, participants ended the study with substantial body weight reduction (9.9%). However, much of their initial improvement in cardiometabolic risk factors had been reversed. Further studies are needed to understand the potential long-term benefits and risks (ie, legacy effects) of such short-term therapy.

The health benefits seen with continued treatment with the maximum tolerated dose of tirzepatide during this study were achieved with a safety profile consistent with that previously reported in SURMOUNT and SURPASS trials and in studies of incretin-based therapies approved for the treatment of obesity and overweight. 18 , 25 - 32

The strengths of this study include its large sample size and the randomized withdrawal design. The duration of the open-label lead-in period allowed the study to assess the maintenance of body weight reduction. Dose escalation protocols during the open-label lead-in period helped to maximize tolerability and reflect dose adjustment strategies that may be helpful to future prescribers.

This study has limitations. First, the design of this study did not allow dose adjustments after randomization and did not evaluate the effects of intensive behavioral therapy on the maintenance of body weight reduction. Second, those who tolerated initial treatment with 10-mg or 15-mg tirzepatide may represent a subgroup of the general population.

After achieving clinically meaningful weight reduction during a 36-week tirzepatide lead-in treatment period, adults with obesity or overweight who continued treatment with maximum tolerated dose tirzepatide for an additional 52 weeks demonstrated superior weight maintenance and continued weight reduction compared to those who switched to placebo.

Accepted for Publication: November 11, 2023.

Published Online: December 11, 2023. doi:10.1001/jama.2023.24945

Corresponding Author: Louis J. Aronne, MD, Comprehensive Weight Control Center, Division of Endocrinology, Diabetes, and Metabolism, Weill Cornell Medicine, 1305 York Ave, Fourth Floor, New York, NY 10065 ( [email protected] ).

Author Contributions: Dr Aronne had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Aronne, Lin, Ahmad, Zhang, Bunck, Murphy.

Acquisition, analysis, or interpretation of data: Aronne, Sattar, Horn, Bays, Wharton, Ahmad, Liao, Bunck, Jouravskaya, Murphy.

Drafting of the manuscript: Aronne, Ahmad, Liao, Bunck, Murphy.

Critical review of the manuscript for important intellectual content: Aronne, Sattar, Horn, Bays, Wharton, Lin, Ahmad, Zhang, Bunck, Jouravskaya, Murphy.

Statistical analysis: Ahmad, Zhang, Liao, Bunck.

Obtained funding: Bunck.

Administrative, technical, or material support: Bays, Lin, Bunck, Murphy.

Supervision: Aronne, Horn, Ahmad, Bunck, Murphy.

Other - Served as a principal investigator in the trial: Horn.

Other - Responsible medical officer for the SURMOUNT program: Bunck.

Conflict of Interest Disclosures: Dr Aronne reported receiving grants or personal fees from Altimmune, AstraZeneca, Boehringer Ingelheim, Eli Lilly, ERX, Gelesis, Intellihealth, Jamieson Wellness, Janssen, Novo Nordisk, Optum, Pfizer, Senda Biosciences and Versanis and being a shareholder of Allurion, ERX Pharmaceuticals, Gelesis, Intellihealth, and Jamieson Wellness. Dr Sattar reported receiving personal fees or grants from Abbott Laboratories, Amgen, AstraZeneca, Boehringer, Eli Lilly, Hanmi Pharmaceuticals, Janssen, Merck Sharp & Dohme, Novartis, Novo Nordisk, Pfizer, Roche, and Sanofi outside the submitted work. Dr Horn reported research funding from Lilly and Novo Nordisk during the conduct of the study and personal fees from Eli Lilly, Novo Nordisk, and Gelesis outside the submitted work. Dr Bays reported receiving grants from Eli Lilly during the conduct of the study and grants from 89 Bio, Alon Medtech/Epitomee, Altimmune, Amgen, Boehringer Ingelheim, Kallyope, Novo Nordisk, Pfizer, Shionogi, Viking, and Vivus and personal fees from Altimmune, Amgen, Boehringer Ingelheim, and Eli Lilly outside the submitted work. Dr Wharton reported receiving nonfinancial support from Eli Lilly during the conduct of the study and personal fees from Novo Nordisk, Boehringer Ingelheim, Biohaven, Bausch Health Canada, and Eli Lilly outside the submitted work. Dr Ahmad reported being an employee and shareholder of Eli Lilly and Company during the conduct of the study. Dr Zhang reported being an employee and shareholder of Eli Lilly and Company during the conduct of the study. Dr Liao reported being an employee and shareholder of Eli Lilly and Company during the conduct of the study. Dr Bunck reported being an employee and shareholder of Eli Lilly and Company during the conduct of the study. Dr Murphy reported being an employee and shareholder of Eli Lilly and Company during the conduct of the study. No other disclosures were reported.

Funding/Support: This study was sponsored by Eli Lilly and Company.

Role of the Funder/Sponsor: Eli Lilly and Company was involved in the study design and conduct; data collection, management, analyses, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The sponsor did not have the right to veto publication or to control the decision regarding to which journal the manuscript was submitted. Final decisions resided with the authors, which included employees of the sponsor.

Group Information: The SURMOUNT-4 Investigators are listed in Supplement 5 .

Meeting Presentation: Part of the data from this study was presented at the 59th European Association for Study of Diabetes; October 2-6, 2023.

Data Sharing Statement: See Supplement 4 .

Additional Contributions: We thank the participants and the study coordinators who cared for them. We thank Amelia Torcello Gomez, PhD, for her writing and editorial assistance, for which she was compensated as part of her salary as an employee of Eli Lilly and Company.

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Ozempic may be linked to condition that causes blindness, but more research is needed

People taking Ozempic and Wegovy may be at increased risk of developing a debilitating eye condition that can cause irreversible vision loss, a study published Wednesday in JAMA Ophthalmology finds. The authors stressed, however, that more studies are needed to confirm a link between the blockbuster drugs and vision problems.

Non-arteritic anterior ischemic optic neuropathy, or NAION , is a condition that affects the optic nerve, a bundle of fibers that connects to the back of the eye and carries signals to the brain so a person can see. In people with NAION, blood flow to the optic nerve gets reduced or blocked, leading to sudden vision loss.

“It is, in effect, a stroke of the optic nerve,” said senior study author Dr. Joseph Rizzo, the director of neuro-ophthalmology at Mass Eye and Ear in Boston.

NAION is the second most common optic nerve disease in the U.S., occurring in up to 10 out of 100,000 people, according to the American Academy of Ophthalmology , and it’s one of the most common causes of sudden blindness. The condition is permanent with no known treatment.

The new study was based on an analysis of medical records spanning six years from more than 16,800 patients in the Boston area, none of whom were initially diagnosed with NAION.

The researchers focused on a subset of those patients — about 1,700 — who either had diabetes, were overweight or had obesity, and compared outcomes after 36 months in those who were prescribed semaglutide to those who weren’t. Semaglutide is the ingredient in Ozempic and Wegovy .

Almost 200 of the diabetes patients were prescribed semaglutide and 17 went on to develop NAION, a rate more than four times higher than those not prescribed the drug. For the obesity group, 361 people were prescribed semaglutide and 20 people developed the condition, a seven times higher rate.

Rizzo said that because the findings were based on a review of existing data, researchers can’t say for sure whether semaglutide causes the eye condition. He said a large, randomized controlled clinical trial is still needed to confirm a link.

“What it does show is an association between taking semaglutide and developing this condition where you lose vision,” he said.

Dr. Andrew Lee, clinical spokesperson for the American Academy of Ophthalmology and a neuro-ophthalmologist at Houston Methodist Hospital, said he's had some patients who developed NAION who were taking semaglutide , but the question was always whether “this is a causal association or merely an association alone.”

People with Type 2 diabetes are already at an increased risk for vision problems, including NAION. Another vision problem, diabetic retinopathy, is the leading cause of blindness in adults and is caused by damage to the retina from high blood sugar levels.

What’s more, risk factors for NAION include sleep apnea and hypertension, which are diseases that are more likely to occur in people with obesity.

Lee said that it’s plausible that weight loss medications could cause the condition, however, it is “premature to conclude” a link based on the single study. “The study can only generate the hypothesis” of a possible link, he said.

There have been some anecdotal reports suggesting that weight loss drugs may be linked to vision problems, including blurred or warped vision.

Rizzo said it’s unclear how the weight loss drugs could cause the condition. It could be due to some mechanism with the class of drugs, called GLP-1s , broadly, he said, or something specific to the way semaglutide works. (The study only looked at semaglutide and not other popular weight loss medications such as tirzepatide, the active ingredient in Eli Lilly’s Mounjaro and Zepbound.)

Dr. Susan Mollan, a consultant neuro-ophthalmologist at the University Hospitals Birmingham in the United Kingdom, wrote in an email that past trials in people with diabetes have shown that when a patient’s blood sugar control is tightened, “they may have a paradoxical worsening of their diabetic retinopathy (temporarily),” so it’s plausible that the GLP-1 drugs, which also help control blood sugar, could “have a paradoxical biological effect.” Mollan wrote an editorial that was published alongside the new study.

Rizzo said patients should speak with their doctor if they are concerned about developing the potential health condition.

“As someone who sees patients who have diseases like this, if someone already has visual loss for whatever reason, and they were wondering whether they would go on semaglutide, I would just have added caution,” Rizzo said.

Dr. Shauna Levy, a specialist in obesity medicine and the medical director of the Tulane Bariatric Center in New Orleans, said the findings won’t change how she prescribes the drugs.

“As for now, the risk still seems low,” she said.

In a statement, a spokesperson for Novo Nordisk said the study is not sufficient to establish a link between semaglutide and the condition.

“Patient safety is a top priority for Novo Nordisk, and we take all reports about adverse events from the use of our medicines very seriously,” the spokesperson said.

Berkeley Lovelace Jr. is a health and medical reporter for NBC News. He covers the Food and Drug Administration, with a special focus on Covid vaccines, prescription drug pricing and health care. He previously covered the biotech and pharmaceutical industry with CNBC.

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Blinding in clinical trials and other studies, simon j day.

a Leo Pharmaceuticals, Princes Risborough, Buckinghamshire HP27 9RR, b ICRF Medical Statistics Group, Institute of Health Sciences, Oxford OX3 7LF

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Medical terms in lay language.

Please use these descriptions in place of medical jargon in consent documents, recruitment materials and other study documents. Note: These terms are not the only acceptable plain language alternatives for these vocabulary words.

This glossary of terms is derived from a list copyrighted by the University of Kentucky, Office of Research Integrity (1990).

For clinical research-specific definitions, see also the Clinical Research Glossary developed by the Multi-Regional Clinical Trials (MRCT) Center of Brigham and Women’s Hospital and Harvard and the Clinical Data Interchange Standards Consortium (CDISC) .

Alternative Lay Language for Medical Terms for use in Informed Consent Documents

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

ABDOMEN/ABDOMINAL body cavity below diaphragm that contains stomach, intestines, liver and other organs ABSORB take up fluids, take in ACIDOSIS condition when blood contains more acid than normal ACUITY clearness, keenness, esp. of vision and airways ACUTE new, recent, sudden, urgent ADENOPATHY swollen lymph nodes (glands) ADJUVANT helpful, assisting, aiding, supportive ADJUVANT TREATMENT added treatment (usually to a standard treatment) ANTIBIOTIC drug that kills bacteria and other germs ANTIMICROBIAL drug that kills bacteria and other germs ANTIRETROVIRAL drug that works against the growth of certain viruses ADVERSE EFFECT side effect, bad reaction, unwanted response ALLERGIC REACTION rash, hives, swelling, trouble breathing AMBULATE/AMBULATION/AMBULATORY walk, able to walk ANAPHYLAXIS serious, potentially life-threatening allergic reaction ANEMIA decreased red blood cells; low red cell blood count ANESTHETIC a drug or agent used to decrease the feeling of pain, or eliminate the feeling of pain by putting you to sleep ANGINA pain resulting from not enough blood flowing to the heart ANGINA PECTORIS pain resulting from not enough blood flowing to the heart ANOREXIA disorder in which person will not eat; lack of appetite ANTECUBITAL related to the inner side of the forearm ANTIBODY protein made in the body in response to foreign substance ANTICONVULSANT drug used to prevent seizures ANTILIPEMIC a drug that lowers fat levels in the blood ANTITUSSIVE a drug used to relieve coughing ARRHYTHMIA abnormal heartbeat; any change from the normal heartbeat ASPIRATION fluid entering the lungs, such as after vomiting ASSAY lab test ASSESS to learn about, measure, evaluate, look at ASTHMA lung disease associated with tightening of air passages, making breathing difficult ASYMPTOMATIC without symptoms AXILLA armpit

BENIGN not malignant, without serious consequences BID twice a day BINDING/BOUND carried by, to make stick together, transported BIOAVAILABILITY the extent to which a drug or other substance becomes available to the body BLOOD PROFILE series of blood tests BOLUS a large amount given all at once BONE MASS the amount of calcium and other minerals in a given amount of bone BRADYARRHYTHMIAS slow, irregular heartbeats BRADYCARDIA slow heartbeat BRONCHOSPASM breathing distress caused by narrowing of the airways

CARCINOGENIC cancer-causing CARCINOMA type of cancer CARDIAC related to the heart CARDIOVERSION return to normal heartbeat by electric shock CATHETER a tube for withdrawing or giving fluids CATHETER a tube placed near the spinal cord and used for anesthesia (indwelling epidural) during surgery CENTRAL NERVOUS SYSTEM (CNS) brain and spinal cord CEREBRAL TRAUMA damage to the brain CESSATION stopping CHD coronary heart disease CHEMOTHERAPY treatment of disease, usually cancer, by chemical agents CHRONIC continuing for a long time, ongoing CLINICAL pertaining to medical care CLINICAL TRIAL an experiment involving human subjects COMA unconscious state COMPLETE RESPONSE total disappearance of disease CONGENITAL present before birth CONJUNCTIVITIS redness and irritation of the thin membrane that covers the eye CONSOLIDATION PHASE treatment phase intended to make a remission permanent (follows induction phase) CONTROLLED TRIAL research study in which the experimental treatment or procedure is compared to a standard (control) treatment or procedure COOPERATIVE GROUP association of multiple institutions to perform clinical trials CORONARY related to the blood vessels that supply the heart, or to the heart itself CT SCAN (CAT) computerized series of x-rays (computerized tomography) CULTURE test for infection, or for organisms that could cause infection CUMULATIVE added together from the beginning CUTANEOUS relating to the skin CVA stroke (cerebrovascular accident)

DERMATOLOGIC pertaining to the skin DIASTOLIC lower number in a blood pressure reading DISTAL toward the end, away from the center of the body DIURETIC "water pill" or drug that causes increase in urination DOPPLER device using sound waves to diagnose or test DOUBLE BLIND study in which neither investigators nor subjects know what drug or treatment the subject is receiving DYSFUNCTION state of improper function DYSPLASIA abnormal cells

ECHOCARDIOGRAM sound wave test of the heart EDEMA excess fluid collecting in tissue EEG electric brain wave tracing (electroencephalogram) EFFICACY effectiveness ELECTROCARDIOGRAM electrical tracing of the heartbeat (ECG or EKG) ELECTROLYTE IMBALANCE an imbalance of minerals in the blood EMESIS vomiting EMPIRIC based on experience ENDOSCOPIC EXAMINATION viewing an internal part of the body with a lighted tube ENTERAL by way of the intestines EPIDURAL outside the spinal cord ERADICATE get rid of (such as disease) Page 2 of 7 EVALUATED, ASSESSED examined for a medical condition EXPEDITED REVIEW rapid review of a protocol by the IRB Chair without full committee approval, permitted with certain low-risk research studies EXTERNAL outside the body EXTRAVASATE to leak outside of a planned area, such as out of a blood vessel

FDA U.S. Food and Drug Administration, the branch of federal government that approves new drugs FIBROUS having many fibers, such as scar tissue FIBRILLATION irregular beat of the heart or other muscle

GENERAL ANESTHESIA pain prevention by giving drugs to cause loss of consciousness, as during surgery GESTATIONAL pertaining to pregnancy

HEMATOCRIT amount of red blood cells in the blood HEMATOMA a bruise, a black and blue mark HEMODYNAMIC MEASURING blood flow HEMOLYSIS breakdown in red blood cells HEPARIN LOCK needle placed in the arm with blood thinner to keep the blood from clotting HEPATOMA cancer or tumor of the liver HERITABLE DISEASE can be transmitted to one’s offspring, resulting in damage to future children HISTOPATHOLOGIC pertaining to the disease status of body tissues or cells HOLTER MONITOR a portable machine for recording heart beats HYPERCALCEMIA high blood calcium level HYPERKALEMIA high blood potassium level HYPERNATREMIA high blood sodium level HYPERTENSION high blood pressure HYPOCALCEMIA low blood calcium level HYPOKALEMIA low blood potassium level HYPONATREMIA low blood sodium level HYPOTENSION low blood pressure HYPOXEMIA a decrease of oxygen in the blood HYPOXIA a decrease of oxygen reaching body tissues HYSTERECTOMY surgical removal of the uterus, ovaries (female sex glands), or both uterus and ovaries

IATROGENIC caused by a physician or by treatment IDE investigational device exemption, the license to test an unapproved new medical device IDIOPATHIC of unknown cause IMMUNITY defense against, protection from IMMUNOGLOBIN a protein that makes antibodies IMMUNOSUPPRESSIVE drug which works against the body's immune (protective) response, often used in transplantation and diseases caused by immune system malfunction IMMUNOTHERAPY giving of drugs to help the body's immune (protective) system; usually used to destroy cancer cells IMPAIRED FUNCTION abnormal function IMPLANTED placed in the body IND investigational new drug, the license to test an unapproved new drug INDUCTION PHASE beginning phase or stage of a treatment INDURATION hardening INDWELLING remaining in a given location, such as a catheter INFARCT death of tissue due to lack of blood supply INFECTIOUS DISEASE transmitted from one person to the next INFLAMMATION swelling that is generally painful, red, and warm INFUSION slow injection of a substance into the body, usually into the blood by means of a catheter INGESTION eating; taking by mouth INTERFERON drug which acts against viruses; antiviral agent INTERMITTENT occurring (regularly or irregularly) between two time points; repeatedly stopping, then starting again INTERNAL within the body INTERIOR inside of the body INTRAMUSCULAR into the muscle; within the muscle INTRAPERITONEAL into the abdominal cavity INTRATHECAL into the spinal fluid INTRAVENOUS (IV) through the vein INTRAVESICAL in the bladder INTUBATE the placement of a tube into the airway INVASIVE PROCEDURE puncturing, opening, or cutting the skin INVESTIGATIONAL NEW DRUG (IND) a new drug that has not been approved by the FDA INVESTIGATIONAL METHOD a treatment method which has not been proven to be beneficial or has not been accepted as standard care ISCHEMIA decreased oxygen in a tissue (usually because of decreased blood flow)

LAPAROTOMY surgical procedure in which an incision is made in the abdominal wall to enable a doctor to look at the organs inside LESION wound or injury; a diseased patch of skin LETHARGY sleepiness, tiredness LEUKOPENIA low white blood cell count LIPID fat LIPID CONTENT fat content in the blood LIPID PROFILE (PANEL) fat and cholesterol levels in the blood LOCAL ANESTHESIA creation of insensitivity to pain in a small, local area of the body, usually by injection of numbing drugs LOCALIZED restricted to one area, limited to one area LUMEN the cavity of an organ or tube (e.g., blood vessel) LYMPHANGIOGRAPHY an x-ray of the lymph nodes or tissues after injecting dye into lymph vessels (e.g., in feet) LYMPHOCYTE a type of white blood cell important in immunity (protection) against infection LYMPHOMA a cancer of the lymph nodes (or tissues)

MALAISE a vague feeling of bodily discomfort, feeling badly MALFUNCTION condition in which something is not functioning properly MALIGNANCY cancer or other progressively enlarging and spreading tumor, usually fatal if not successfully treated MEDULLABLASTOMA a type of brain tumor MEGALOBLASTOSIS change in red blood cells METABOLIZE process of breaking down substances in the cells to obtain energy METASTASIS spread of cancer cells from one part of the body to another METRONIDAZOLE drug used to treat infections caused by parasites (invading organisms that take up living in the body) or other causes of anaerobic infection (not requiring oxygen to survive) MI myocardial infarction, heart attack MINIMAL slight MINIMIZE reduce as much as possible Page 4 of 7 MONITOR check on; keep track of; watch carefully MOBILITY ease of movement MORBIDITY undesired result or complication MORTALITY death MOTILITY the ability to move MRI magnetic resonance imaging, diagnostic pictures of the inside of the body, created using magnetic rather than x-ray energy MUCOSA, MUCOUS MEMBRANE moist lining of digestive, respiratory, reproductive, and urinary tracts MYALGIA muscle aches MYOCARDIAL pertaining to the heart muscle MYOCARDIAL INFARCTION heart attack

NASOGASTRIC TUBE placed in the nose, reaching to the stomach NCI the National Cancer Institute NECROSIS death of tissue NEOPLASIA/NEOPLASM tumor, may be benign or malignant NEUROBLASTOMA a cancer of nerve tissue NEUROLOGICAL pertaining to the nervous system NEUTROPENIA decrease in the main part of the white blood cells NIH the National Institutes of Health NONINVASIVE not breaking, cutting, or entering the skin NOSOCOMIAL acquired in the hospital

OCCLUSION closing; blockage; obstruction ONCOLOGY the study of tumors or cancer OPHTHALMIC pertaining to the eye OPTIMAL best, most favorable or desirable ORAL ADMINISTRATION by mouth ORTHOPEDIC pertaining to the bones OSTEOPETROSIS rare bone disorder characterized by dense bone OSTEOPOROSIS softening of the bones OVARIES female sex glands

PARENTERAL given by injection PATENCY condition of being open PATHOGENESIS development of a disease or unhealthy condition PERCUTANEOUS through the skin PERIPHERAL not central PER OS (PO) by mouth PHARMACOKINETICS the study of the way the body absorbs, distributes, and gets rid of a drug PHASE I first phase of study of a new drug in humans to determine action, safety, and proper dosing PHASE II second phase of study of a new drug in humans, intended to gather information about safety and effectiveness of the drug for certain uses PHASE III large-scale studies to confirm and expand information on safety and effectiveness of new drug for certain uses, and to study common side effects PHASE IV studies done after the drug is approved by the FDA, especially to compare it to standard care or to try it for new uses PHLEBITIS irritation or inflammation of the vein PLACEBO an inactive substance; a pill/liquid that contains no medicine PLACEBO EFFECT improvement seen with giving subjects a placebo, though it contains no active drug/treatment PLATELETS small particles in the blood that help with clotting POTENTIAL possible POTENTIATE increase or multiply the effect of a drug or toxin (poison) by giving another drug or toxin at the same time (sometimes an unintentional result) POTENTIATOR an agent that helps another agent work better PRENATAL before birth PROPHYLAXIS a drug given to prevent disease or infection PER OS (PO) by mouth PRN as needed PROGNOSIS outlook, probable outcomes PRONE lying on the stomach PROSPECTIVE STUDY following patients forward in time PROSTHESIS artificial part, most often limbs, such as arms or legs PROTOCOL plan of study PROXIMAL closer to the center of the body, away from the end PULMONARY pertaining to the lungs

QD every day; daily QID four times a day

RADIATION THERAPY x-ray or cobalt treatment RANDOM by chance (like the flip of a coin) RANDOMIZATION chance selection RBC red blood cell RECOMBINANT formation of new combinations of genes RECONSTITUTION putting back together the original parts or elements RECUR happen again REFRACTORY not responding to treatment REGENERATION re-growth of a structure or of lost tissue REGIMEN pattern of giving treatment RELAPSE the return of a disease REMISSION disappearance of evidence of cancer or other disease RENAL pertaining to the kidneys REPLICABLE possible to duplicate RESECT remove or cut out surgically RETROSPECTIVE STUDY looking back over past experience

SARCOMA a type of cancer SEDATIVE a drug to calm or make less anxious SEMINOMA a type of testicular cancer (found in the male sex glands) SEQUENTIALLY in a row, in order SOMNOLENCE sleepiness SPIROMETER an instrument to measure the amount of air taken into and exhaled from the lungs STAGING an evaluation of the extent of the disease STANDARD OF CARE a treatment plan that the majority of the medical community would accept as appropriate STENOSIS narrowing of a duct, tube, or one of the blood vessels in the heart STOMATITIS mouth sores, inflammation of the mouth STRATIFY arrange in groups for analysis of results (e.g., stratify by age, sex, etc.) STUPOR stunned state in which it is difficult to get a response or the attention of the subject SUBCLAVIAN under the collarbone SUBCUTANEOUS under the skin SUPINE lying on the back SUPPORTIVE CARE general medical care aimed at symptoms, not intended to improve or cure underlying disease SYMPTOMATIC having symptoms SYNDROME a condition characterized by a set of symptoms SYSTOLIC top number in blood pressure; pressure during active contraction of the heart

TERATOGENIC capable of causing malformations in a fetus (developing baby still inside the mother’s body) TESTES/TESTICLES male sex glands THROMBOSIS clotting THROMBUS blood clot TID three times a day TITRATION a method for deciding on the strength of a drug or solution; gradually increasing the dose T-LYMPHOCYTES type of white blood cells TOPICAL on the surface TOPICAL ANESTHETIC applied to a certain area of the skin and reducing pain only in the area to which applied TOXICITY side effects or undesirable effects of a drug or treatment TRANSDERMAL through the skin TRANSIENTLY temporarily TRAUMA injury; wound TREADMILL walking machine used to test heart function

UPTAKE absorbing and taking in of a substance by living tissue

VALVULOPLASTY plastic repair of a valve, especially a heart valve VARICES enlarged veins VASOSPASM narrowing of the blood vessels VECTOR a carrier that can transmit disease-causing microorganisms (germs and viruses) VENIPUNCTURE needle stick, blood draw, entering the skin with a needle VERTICAL TRANSMISSION spread of disease

WBC white blood cell

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Study links Ozempic to higher risk of eye condition that can cause vision loss

By Elaine Chen July 3, 2024

A congested optic disc — biotech coverage from STAT

A new observational study on Wednesday reported for the first time a potential link between Novo Nordisk’s GLP-1 drugs Ozempic and Wegovy and an eye condition that can cause vision loss.

After hearing anecdotes of patients on the diabetes and obesity drugs experiencing nonarteritic anterior ischemic optic neuropathy, or NAION, researchers at Massachusetts Eye and Ear analyzed data from a registry of patients at their institution to see if there was a broad trend.

Among 710 patients with type 2 diabetes, there were 17 cases of NAION in patients prescribed semaglutide (the scientific name of both drugs). This translated to a cumulative rate of 8.9% over three years. That compares with six cases in patients prescribed non-GLP-1 diabetes drugs, calculated as a cumulative rate of 1.8%. Through statistical analyses, the researchers estimate that there was a 4.28 times greater risk of developing the condition in patients prescribed semaglutide, according to the study, published in JAMA Ophthalmology .

Studying 979 patients who had overweight or obesity, researchers found 20 cases of NAION in people prescribed semaglutide, calculated as a cumulative rate of 6.7%. In comparison, there were three cases in people prescribed non-GLP-1 obesity drugs, calculated as a cumulative rate of 0.8%. The researchers estimate that there was a 7.64 times greater risk of developing the condition in patients prescribed semaglutide.

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In both the diabetes and obesity cohorts that got semaglutide, the researchers found that NAION cases occurred most frequently in the first year after the medications were prescribed.

NAION, sometimes referred to as an “eye stroke,” occurs from a lack of sufficient blood flow to the optic nerve. It typically causes sudden vision loss and, in severe cases, can lead to blindness. There are currently no proven treatments for the condition.

NAION was not reported as an adverse event in trials studying semaglutide. As drugs go on the market and many more people take them, it’s common for doctors to find new rare side effects that weren’t seen in the controlled studies.

The authors stress, though, that this observational study does not show that the drugs cause higher rates of NAION — just that there is a potential association. The study also focused only on semaglutide, and the findings can’t be immediately generalized to other GLP-1 drugs. The researchers still believed it’s important to report the findings to spur further analysis and to make doctors and patients aware.

“This study shows an association that we didn’t know about before, and what it should do is give people added information to make a good, well-informed decision about them taking the medicine,” said Joseph Rizzo, senior author on the study and a neuro-ophthalmologist at Mass Eye and Ear. “It doesn’t mean they shouldn’t take it. People are going to have a different sense of risk about whether they want to take it or not.”

In a statement, Novo said that “patient safety is a top priority” and that it takes “all reports about adverse events from use of our medicines very seriously.” NAION is not an adverse event listed on the approved labels, and the new study was not designed to establish a causal relationship, the company said.

“Semaglutide has been studied in large real world evidence studies and robust clinical development programs,” Novo added. “The totality of data provides reassurance of the safety profile of semaglutide.”

Related: New obesity drugs are seemingly everywhere. Black Americans feel left out

There were several limitations to the study. While the researchers manually reviewed each case of NAION, they did not report how severe the cases of NAION were and how much vision people lost. Rizzo said a much larger study would be needed to power that kind of analysis.

Additionally, while the researchers confirmed that the prescribed doses of semaglutide were dispensed, they could not track whether patients actually took the medications and adhered to them.

There may also have been confounding variables. Type 2 diabetes and high blood pressure are risk factors for NAION. It’s possible that patients with more severe diabetes or high blood pressure were more likely to be prescribed semaglutide, as it’s a highly effective drug, and that may have played into the higher rates of NAION in those cohorts.

Susan Mollan, a consultant neuro-opthalmologist at University Hospitals Birmingham in the U.K., who wasn’t involved in the study, said the findings are somewhat surprising since GLP-1 drugs have been found in early studies to reduce inflammation and protect nerves.

She said it’s important in future research to determine whether higher rates of NAION may be linked to direct effects of the drugs or to the biological changes in people taking the drugs.

For example, researchers have seen in trials that people taking semaglutide had higher rates of a different eye condition called diabetic retinopathy. Although study of the condition is ongoing, researchers believe it may be caused by a rapid decrease in people’s blood sugar levels. Mollan said it’s possible that the normalization of people’s metabolic systems, rather than the drugs itself, may help explain the NAION cases.

Overall, though, Mollan said the new study is “a very important paper.”

“When you first get these kinds of signals, we need to apply caution to what we’re hearing, but also we need to protect our patients,” she added. “I’d want eye doctors, endocrinology doctors, and physicians that prescribe the drug to be aware that this has been reported and await further data of a population study to help us talk about the risk.”

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Climate change ignored? U-M study reveals sociology’s blind spot

July 1, 2024

Climate change is a social crisis. Societies drive climate change, bear the brunt of its effects, and carry alone the task of responding. Yet a new University of Michigan study finds a peculiar and pervasive lack of attention to climate change in the field of sociology, a field that can uniquely attend to these issues.

Sofia Hiltner , a PhD candidate in sociology at the University of Michigan and a predoctoral trainee of the Population Studies Center at the Institute for Social Research, is the sole author of the most expansive and up-to-date assessment of attention to climate change in sociology in the United States, just out in The American Sociologist .

Her rigorous review finds little to no mention of climate change across leading sociology journal articles (0.89%), conference sessions (1.5%), and faculty biographies (2.8%) and course listings in top-ranked departments in the U.S. (0.2%).

The study indicates a critical need for sociological insight into combating climate change. It finds little to no mention of climate change across leading sociology journal articles (0.9%), conference sessions (1.5%), faculty biographies (2.8%) and course listings in top-ranked departments in the U.S. (0.2%).

“This deficit threatens sociology’s relevance to human welfare. It also limits our understanding of the climate crisis as a social problem and our ability to imagine responses,” said Hiltner, a predoctoral trainee at the Population Studies Center at the Institute for Social Research.

Hiltner’s research extends previous work on sociology’s attention to climate change by examining a wider range of forums in which engagement might take place—articles from a range of leading, generalist sociology journals, conference sessions from the American Sociological Association Annual Meeting, and faculty biographies and course offerings in the U.S. News 20 top-ranked sociology departments.

“These last measures are critical because they provide insight into the education of students—the sociologists of the future,” Hiltner said.

Hiltner found limited attention to climate change across all of these forums. She found that less than 1% of articles (38 of 4,288) in sociology journals were substantially about climate change. Only two major sessions of the ASA Annual Meeting since 2002 mentioned climate change.

If a prospective student were to consider attending a top-20 sociology program in the U.S., they would find only three departments with more than one faculty member who mentioned climate change as a focus in their profile. And, out of more than 8,000 courses offered in these departments from fall 2019 to spring 2023, only 0.2% were concerned with climate change, while 1.4% mentioned environmental topics more generally.

Climate change is intertwined with national and global patterns of inequality that sociology, as a discipline, may be particularly suited to assess. Research shows that marginalized populations in the U.S. and worldwide experience more significant impacts from climate-related threats such as extreme heat, storms, air pollution and floods.

“Sociology is good at documenting disparities along multiple dimensions, and thus should be part of the conversation,” said Elizabeth Armstrong, U-M professor of sociology and an affiliate of the Population Studies Center. “Sociologists are also very good at analyzing why social change is hard to accomplish and can contribute to explaining why the policy changes necessary to make large-scale structural change are so hard to accomplish.”

Sociology is scarce in climate change research and major assessments, such as the U.S. Intergovernmental Panel on Climate Change reports. Social scientists have long lamented the dominance of the natural sciences—and within the relatively small space occupied by the social sciences, the dominance of economics—in climate research and policymaking.

“With this work, I hope to spark a conversation about the reasons for sociology’s relative silence on climate change, what is at stake, and ways forward,” Hiltner said.

Going forward, Hiltner suggests sociologists consider how climate change relates to their areas of interest and promote attention to climate change in the education of students and future sociologists. On the other hand, funders, journalists and policymakers may do more to foster and integrate the perspectives of social scientists on climate change, she says.

Written by Tevah Platt, U-M Institute for Social Research

Contact: Fernanda Pires

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What Is a Double-Blind Study?
PPT
Double Blind Study
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Blinding in Clinical Trials: Seeing the Big Picture
1. Introduction. Randomized clinical trials are a gold standard in evidence-based medicine because findings from these studies reflect the highest possible level of evidence which may be garnered from an original research study [].Randomized clinical trials tend to be highly tailored to a specific research question but, for a vast majority of interventions and outcomes, blinding is widely ...
Single, Double & Triple Blind Study
A double-blind study withholds each subject's group assignment from both the participant and the researcher performing the experiment. If participants know which group they are assigned to, there is a risk that they might change their behavior in a way that would influence the results. This can lead to a few types of research bias ...
Blinding: Who, what, when, why, how?
Blinding refers to the concealment of group allocation from one or more individuals involved in a clinical research study, most commonly a randomized controlled trial (RCT). Although randomization minimizes differences between treatment groups at the outset of the trial, it does nothing to prevent differential treatment of the groups later in ...
Blinded experiment
In medical research, the terms single-blind, double-blind and triple-blind are commonly used to describe blinding. These terms describe experiments in which (respectively) one, two, or three parties are blinded to some information. Most often, single-blind studies blind patients to their treatment allocation, double-blind studies blind both ...
Double-Blind Study
A double-blind study blinds both the subjects as well as the researchers to the treatment allocation. Triple-blinding involves withholding this information from the patients, researchers, as well as data analysts.Randomized, double-blind placebo-controlled trials involve the random placement of participants into two groups; an experimental ...
Blinding in clinical trials and other studies
Human behaviour is influenced by what we know or believe. In research there is a particular risk of expectation influencing findings, most obviously when there is some subjectivity in assessment, leading to biased results. Blinding (sometimes called masking) is used to try to eliminate such bias. It is a tenet of randomised controlled trials that the treatment allocation for each patient is ...
Blinding: A detailed guide for students
However, it can be difficult to blind the surgeon to the tested intervention as they must perform the procedure. This still remains a challenge in medical research. (Although it is not impossible to blind surgeons: have a look at this for more information). It is recommended that the groups are treated as equally as possible, blinding should be ...
Blinding in randomised trials: hiding who got what
Blinding embodies a rich history spanning over two centuries. Most researchers worldwide understand blinding terminology, but confusion lurks beyond a general comprehension. Terms such as single blind, double blind, and triple blind mean different things to different people. Moreover, many medical researchers confuse blinding with allocation concealment. Such confusion indicates ...
Blinding: who, when and how? importance and impact on findings
Blinding is an important tool used in designing research studies to minimize any type of bias that may occur. Minimizing bias is vital to improving the validity of study outcomes and the study's future implications. Blinding of researchers and participants may be difficult in surgical trials; however, maintaining objective and independent study ...
Blinding: an essential component in decreasing risk of bias in
Blinding (or masking) is the process used in experimental research by which study participants, persons caring for the participants, persons providing the intervention, data collectors and data analysts are kept unaware of group assignment (control vs intervention). Blinding aims to reduce the risk of bias that can be caused by an awareness of group assignment.
Double-Blind Experimental Study And Procedure Explained
Double-blind studies are considered the gold standard in research because they help to control for experimental biases arising from the subjects' expectations and experimenter biases that emerge when the researchers unknowingly influence how the subjects respond or how the data is collected. Using the double-blind method improves the ...
Blind Study in Research
In a single-blind study, the researcher knows which group each participant is assigned to and the variable being evaluated. A single-blind experiment is important if the research subjects are ...
Double Blind Study
Triple Blind Study. A triple blind study includes an additional level of blinding. So, the data analysis team or the group overseeing an experiment is blind, in addition to the researchers and subjects. Example: Vaccine Study. Triple blind studies are common as part of the vaccine approval process.
(PDF) Blinding in Randomized Controlled Trials: What ...
Randomized Controlled Trial (RCT) is a classical research design in which the participants are randomly allocated to one or other treatment conditions under the study. Researchers widely use ...
Information Blocking in a Blinded Study
Single Blind Studies: A research study done in such a way that the participants do not know (are blinded to) what treatment they are receiving to ensure the study results are not biased (the power of suggestion). ... For all research studies, "Research Visit Notes" entered in MiChart are not visible to patients during the conduct of a study
Global estimates on the number of people blind or visually ...
This study contributes to the expanding body of literature on the burden of DR, highlighting the need for increased global attention and investment in this research area. ... people blind or ...
Recruitment, Retention, and Blinding in Clinical Trials
Blinding in Clinical Trials. Blinding is necessary for control of bias in clinical trials. We define blinding as the process of concealing research design elements such as group assignment, treatment agent, and research hypotheses from participants, health care providers, or data collectors ( Penson & Wei, 2006; Portney & Watkins, 2000 ).
Video: Blind Study in Research
Read about the blind study and the need for blinding in research. Understand single-blind study and double-blind study. See examples of both blind...
Blinding and Its Types in Research
There are three types of blinding: Single Blinded Trial. Double-Blinded Trial. Triple Blinded Trial. 1. Single Blinded Trial: In a single-blinded trial, blinding or masking of any one group is ensured. Usually, the participant is blinded in a single-blinded trial as they are the ones receiving treatment. Example….
Double-Blind Studies in Research
A double-blind study is one in which neither the participants nor the experimenters know who is receiving a particular treatment. This procedure is utilized to prevent bias in research results. Double-blind studies are particularly useful for preventing bias due to demand characteristics or the placebo effect .
What is 'Blinding' in Research? What are Its Types?
Blinding, in research, mentions to a practice where the study population or the stakeholders involved in research are not permitted from knowing certain information or treatment, which may somehow influence the study findings. ... If both 'the participants' and 'the study staffs' are blinded, it is known as a double- blind study. 3 ...
Why is blinding important in research?
Blinding is important to reduce research bias (e.g., observer bias, demand characteristics) and ensure a study's internal validity. If participants know whether they are in a control or treatment group, they may adjust their behavior in ways that affect the outcome that researchers are trying to measure.
Double Blind Studies in Research: Types, Pros & Cons
There are three types of blind studies namely single-blind study, double-blind study, and triple-blind study. 1. Single-blind study: in this type of blind study only the subjects in the experiment are prevented from knowing the treatment they are given. The single-blind study is also known as the single masked study. 2.
Continued Treatment With Tirzepatide for Maintenance of Weight
eFigure 4. Time, during the 52-week double-blind period (week 36 to 88 in the entire study), to first occurrence of participant returning to >95% baseline body weight if already lost ≥5% since week 0. eFigure 5. Box plot of the percent change in body weight over time during the entire study. eFigure 6.
Ozempic may be linked to condition that causes blindness, but more
People taking Ozempic and Wegovy may be at increased risk of developing a debilitating eye condition that can cause irreversible vision loss, a study published in JAMA Ophthalmology finds.
Statistics Notes: Blinding in clinical trials and other studies
Blinding in clinical trials and other studies. Human behaviour is influenced by what we know or believe. In research there is a particular risk of expectation influencing findings, most obviously when there is some subjectivity in assessment, leading to biased results. Blinding (sometimes called masking) is used to try to eliminate such bias.
Medical Terms in Lay Language
Human Subjects Office / IRB Hardin Library, Suite 105A 600 Newton Rd Iowa City, IA 52242-1098. Voice: 319-335-6564 Fax: 319-335-7310
Study: Ozempic linked to eye condition that can cause vision loss
A new observational study on Wednesday reported for the first time a potential link between Novo Nordisk's GLP-1 drugs Ozempic and Wegovy and an eye condition that can cause vision loss.. After ...
Climate change ignored? U-M study reveals sociology's blind spot
Sofia Hiltner, a PhD candidate in sociology at the University of Michigan and a predoctoral trainee of the Population Studies Center at the Institute for Social Research, is the sole author of the most expansive and up-to-date assessment of attention to climate change in sociology in the United States, just out in The American Sociologist.

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Blinding in clinical trials and other studies

Blinding: A detailed guide for students

What is blinding?

There are a number of different levels of blinding:

Common methods of blinding

Why is blinding important?

What about when blinding is not possible?

Allocation concealment

References (pdf)

Saul Crandon

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What is blinding?

Why incorporate blinding?

How to implement blinding?

How to assess if blinding has been successful?

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Double-Blind Experimental Study And Procedure Explained

What is a Blinded Study?

Double-Blind Studies

Example Double-Blind Studies

Double-Blinding Procedure

For Example

Reduces risk of bias

Results can be duplicated

It tests for three groups

Applicable across multiple industries

Disadvantages

Small Sample Size

Negative Reaction to Placebo

It doesn’t reflect real-life circumstances

What is the difference between a single-blind, double-blind, and triple-blind study?

Is a double-blind study the same as a randomized clinical trial?

Are double-blind studies ethical?

What is the purpose of randomization using double blinding?

Why are double-blind experiments considered the gold standard?

Can blinding be used in qualitative studies?

Double Blind Study – Blinded Experiments

Single Blind, Double Blind, and Triple Blind Studies

Single Blind Study

Double Blind Study

Triple Blind Study

Uses of Blind Studies

Related Posts

Information Blocking in a Blinded Study

Limiting a Research Participant's View of MyUofMHealth (Patient Portal)

What actions should be taken to prevent participants from seeing their test results?

What IRBMED procedures do I need to follow when a research study limits access to the Patient Portal?

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Vision Loss Expert Group of the Global Burden of Disease Study

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