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• What Is a Cohort Study? | Definition & Examples

What Is a Cohort Study? | Definition & Examples

Published on February 24, 2023 by Tegan George .

A cohort study is a type of observational study that follows a group of participants over a period of time, examining how certain factors (like exposure to a given risk factor) affect their health outcomes. The individuals in the cohort have a characteristic or lived experience in common, such as birth year or geographic area.

While there are several types of cohort study—including open, closed, and dynamic—there are two that are particularly common: prospective cohort studies and retrospective cohort studies .

The initial cohort consisted of about 18,000 newborns. They were enrolled in the study shortly after birth, with regular follow-ups, medical examinations, and cognitive assessments to track their physical, social, and cognitive development.

Cohort studies are particularly useful for identifying risk factors for diseases. They can help researchers identify potential interventions to help prevent or treat the disease, and are often used in fields like medicine or healthcare research.

Table of contents

When to use a cohort study, examples of cohort studies, advantages and disadvantages of cohort studies, frequently asked questions.

Cohort studies are a type of observational study that can be qualitative or quantitative in nature. They can be used to conduct both exploratory research and explanatory research depending on the research topic.

In prospective cohort studies , data is collected over time to compare the occurrence of the outcome of interest in those who were exposed to the risk factor and those who were not. This can help ascertain whether the risk factor could be associated with the outcome.

In retrospective cohort studies , your participants must already possess the disease or health outcome being studied prior to joining. The study is then focused on analyzing the health outcomes of those who share the exposure to the risk factor over a period of time.

A cohort study could be a good fit for your research if:

• You have access to a large pool of research subjects and are comfortable and able to fund research stretching over a longer timeline.
• The relationship between the exposure and health outcome you’re studying is not well understood, and/or its long-term effects have not been thoroughly investigated.
• The exposure you’re studying is rare, or there are possible ethical considerations preventing you from a traditional experimental design .
• Cohort studies in general are more longitudinal in nature. They usually follow the group studied over a long period of time, investigating how certain factors affect their health outcomes.
• Case–control studies rely on primary research , comparing a group of participants already possessing a condition of interest to a control group lacking that condition in real time.

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Cohort studies are common in fields like medicine, epidemiology, and healthcare.

Cohort studies are a strong research method , particularly in epidemiology, health, and medicine, but they are not without their disadvantages.

Advantages of cohort studies

Advantages of cohort studies include:

• Cohort studies are better able to approach an estimation of causality than other types of observational studies. Due to their ability to establish temporality, multiple outcomes, and disease incidence over time, researchers are able to determine with more certainty that the exposure indeed preceded the outcome. This strengthens a claim for a cause-and-effect relationship between the variables of interest.
• Due to their long nature, cohort studies are a particularly good choice for studying rare exposures , such as exposure to a new drug or an environmental toxin. Other research designs aren’t able to incorporate the breadth and depth of the impact as broadly as cohort studies do.
• Because cohort studies usually rely on large groups of participants, they are better able to control for potentially confounding variables , such as age, gender identity, or socioeconomic status. Relatedly, the ability to use a sampling method that ensures a more representative sample of the population leads to findings that are typically much more generalizable , with higher internal validity and external validity .

Disadvantages of cohort studies

Disadvantages of cohort studies include:

• Cohort studies can be extremely time-consuming and expensive to conduct due to their long and intense nature.
• Cohort studies are at risk for biases inherent to long-term studies like attrition bias and survivorship bias , as participants are likely to drop out over time. Measurement errors like omitted variable bias and information bias can also confound your analysis, leading you to draw conclusions that may not be true.
• Like many other experimental designs , cohort studies can raise questions regarding ethical considerations . This is particularly the case if the exposure of interest is harmful, or if there is no known treatment for it. Prior to beginning your research, it is critical to ensure that participation in your study is fully voluntary, informed, and as safe as it can be for your research subjects.

The easiest way to remember the difference between prospective and retrospective cohort studies is timing. 

• A prospective cohort study moves forward in time, following a group of participants to track the development of an outcome of interest.
• A retrospective cohort study moves backward in time, first identifying a group of people who already possess the outcome of interest, and then looking backwards to assess their exposure to a risk factor.

A closed cohort study is a type of cohort study where all participants are selected at the beginning of the study, with no new participants added during any of the follow-up periods.

This approach is useful when the exposure being studied is rare, or when it isn’t practically or financially feasible to recruit new participants.

In a cohort study , the incidence refers to the number of new cases of a disease or health outcome that develop during the study period, while prevalence refers to the proportion of the population who have the disease or health outcome at a given point in time. Cohort studies are particularly useful for measuring incidence rates.

A dynamic cohort study is a type of cohort study where the participants are not fixed at the start of the study. Instead, new participants can be added over time if they become eligible to participate. This approach is useful when the study population is expected to change over time.

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

George, T. (2023, February 24). What Is a Cohort Study? | Definition & Examples. Scribbr. Retrieved June 24, 2024, from https://www.scribbr.com/methodology/cohort-study/

Euser, A. M., Zoccali, C., Jager, K. J., & Dekker, F. W. (2009). Cohort Studies: Prospective versus Retrospective. Nephron Clinical Practice , 113 (3), c214–c217. https://doi.org/10.1159/000235241

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Study Design 101: Cohort Study

• Case Report
• Case Control Study
• Cohort Study
• Randomized Controlled Trial
• Practice Guideline
• Systematic Review
• Meta-Analysis
• Helpful Formulas
• Finding Specific Study Types

A study design where one or more samples (called cohorts) are followed prospectively and subsequent status evaluations with respect to a disease or outcome are conducted to determine which initial participants exposure characteristics (risk factors) are associated with it. As the study is conducted, outcome from participants in each cohort is measured and relationships with specific characteristics determined

• Subjects in cohorts can be matched, which limits the influence of confounding variables
• Standardization of criteria/outcome is possible
• Easier and cheaper than a randomized controlled trial (RCT)

Disadvantages

• Cohorts can be difficult to identify due to confounding variables
• No randomization, which means that imbalances in patient characteristics could exist
• Blinding/masking is difficult
• Outcome of interest could take time to occur

Design pitfalls to look out for

The cohorts need to be chosen from separate, but similar, populations.

How many differences are there between the control cohort and the experiment cohort? Will those differences cloud the study outcomes?

Fictitious Example

A cohort study was designed to assess the impact of sun exposure on skin damage in beach volleyball players. During a weekend tournament, players from one team wore waterproof, SPF 35 sunscreen, while players from the other team did not wear any sunscreen. At the end of the volleyball tournament players' skin from both teams was analyzed for texture, sun damage, and burns. Comparisons of skin damage were then made based on the use of sunscreen. The analysis showed a significant difference between the cohorts in terms of the skin damage.

Real-life Examples

Hoepner, L., Whyatt, R., Widen, E., Hassoun, A., Oberfield, S., Mueller, N., ... Rundle, A. (2016). Bisphenol A and Adiposity in an Inner-City Birth Cohort. Environmental Health Perspectives, 124 (10), 1644-1650. https://doi.org/10.1289/EHP205

This longitudinal cohort study looked at whether exposure to bisphenol A (BPA) early in life affects obesity levels in children later in life. Positive associations were found between prenatal BPA concentrations in urine and increased fat mass index, percent body fat, and waist circumference at age seven.

Lao, X., Liu, X., Deng, H., Chan, T., Ho, K., Wang, F., ... Yeoh, E. (2018). Sleep Quality, Sleep Duration, and the Risk of Coronary Heart Disease: A Prospective Cohort Study With 60,586 Adults. Journal Of Clinical Sleep Medicine, 14 (1), 109-117. https://doi.org/10.5664/jcsm.6894

This prospective cohort study explored "the joint effects of sleep quality and sleep duration on the development of coronary heart disease." The study included 60,586 participants and an association was shown between increased risk of coronary heart disease and individuals who experienced short sleep duration and poor sleep quality. Long sleep duration did not demonstrate a significant association.

Related Formulas

• Relative Risk

Related Terms

A group that shares the same characteristics among its members (population).

Confounding Variables

Variables that cause/prevent an outcome from occurring outside of or along with the variable being studied. These variables render it difficult or impossible to distinguish the relationship between the variable and outcome being studied).

Population Bias/Volunteer Bias

A sample may be skewed by those who are selected or self-selected into a study. If only certain portions of a population are considered in the selection process, the results of a study may have poor validity.

Prospective Study

A study that moves forward in time, or that the outcomes are being observed as they occur, as opposed to a retrospective study, which looks back on outcomes that have already taken place.

Now test yourself!

1. In a cohort study, an exposure is assessed and then participants are followed prospectively to observe whether they develop the outcome.

a) True b) False

2. Cohort Studies generally look at which of the following?

a) Determining the sensitivity and specificity of diagnostic methods b) Identifying patient characteristics or risk factors associated with a disease or outcome c) Variations among the clinical manifestations of patients with a disease d) The impact of blinding or masking a study population

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Quantitative study designs: Cohort Studies

Quantitative study designs.

• Introduction
• Cohort Studies
• Randomised Controlled Trial
• Case Control
• Cross-Sectional Studies
• Study Designs Home

Cohort Study

Did you know that the majority of people will develop a diagnosable mental illness whilst only a minority will experience enduring mental health?  Or that groups of people at risk of having high blood pressure and other related health issues by the age of 38 can be identified in childhood?  Or that a poor credit rating can be indicative of a person’s health status?

These findings (and more) have come out of a large cohort study started in 1972 by researchers at the University of Otago in New Zealand.  This study is known as The Dunedin Study and it has followed the lives of 1037 babies born between 1 April 1972 and 31 March 1973 since their birth. The study is now in its fifth decade and has produced over 1200 publications and reports, many of which have helped inform policy makers in New Zealand and overseas.

In Introduction to Study Designs, we learnt that there are many different study design types and that these are divided into two categories:  Experimental and Observational. Cohort Studies are a type of observational study. 

What is a Cohort Study design?

• Cohort studies are longitudinal, observational studies, which investigate predictive risk factors and health outcomes. 
• They differ from clinical trials, in that no intervention, treatment, or exposure is administered to the participants. The factors of interest to researchers already exist in the study group under investigation.
• Study participants are observed over a period of time. The incidence of disease in the exposed group is compared with the incidence of disease in the unexposed group.
• Because of the observational nature of cohort studies they can only find correlation between a risk factor and disease rather than the cause. 

Cohort studies are useful if:

• There is a persuasive hypothesis linking an exposure to an outcome.
• The time between exposure and outcome is not too long (adding to the study costs and increasing the risk of participant attrition).
• The outcome is not too rare.

The stages of a Cohort Study

• A cohort study starts with the selection of a group of participants (known as a ‘cohort’) sourced from the same population, who must be free of the outcome under investigation but have the potential to develop that outcome.
• The participants must be identical, having common characteristics except for their exposure status.
• The participants are divided into two groups – the first group is the ‘exposure’ group, the second group is free of the exposure. 

Types of Cohort Studies

There are two types of cohort studies:  Prospective and Retrospective .

How Cohort Studies are carried out

Adapted from: Cohort Studies: A brief overview by Terry Shaneyfelt [video] https://www.youtube.com/watch?v=FRasHsoORj0)

Which clinical questions does this study design best answer?

What are the advantages and disadvantages to consider when using a Cohort Study?

What does a strong Cohort Study look like?

• The aim of the study is clearly stated.
• It is clear how the sample population was sourced, including inclusion and exclusion criteria, with justification provided for the sample size.  The sample group accurately reflects the population from which it is drawn.
• Loss of participants to follow up are stated and explanations provided.
• The control group is clearly described, including the selection methodology, whether they were from the same sample population, whether randomised or matched to minimise bias and confounding.
• It is clearly stated whether the study was blinded or not, i.e. whether the investigators were aware of how the subject and control groups were allocated.
• The methodology was rigorously adhered to.
• Involves the use of valid measurements (recognised by peers) as well as appropriate statistical tests.
• The conclusions are logically drawn from the results – the study demonstrates what it says it has demonstrated.
• Includes a clear description of the data, including accessibility and availability.

What are the pitfalls to look for?

• Confounding factors within the sample groups may be difficult to identify and control for, thus influencing the results.
• Participants may move between exposure/non-exposure categories or not properly comply with methodology requirements.
• Being in the study may influence participants’ behaviour.
• Too many participants may drop out, thus rendering the results invalid.

Critical appraisal tools

To assist with the critical appraisal of a cohort study here are some useful tools that can be applied.

Critical appraisal checklist for cohort studies (JBI)

CASP appraisal checklist for cohort studies

Real World Examples

Bell, A.F., Rubin, L.H., Davis, J.M., Golding, J., Adejumo, O.A. & Carter, C.S. (2018). The birth experience and subsequent maternal caregiving attitudes and behavior: A birth cohort study . Archives of Women’s Mental Health .

Dykxhoorn, J., Hatcher, S., Roy-Gagnon, M.H., & Colman, I. (2017). Early life predictors of adolescent suicidal thoughts and adverse outcomes in two population-based cohort studies . PLoS ONE , 12(8).

Feeley, N., Hayton, B., Gold, I. & Zelkowitz, P. (2017). A comparative prospective cohort study of women following childbirth: Mothers of low birthweight infants at risk for elevated PTSD symptoms . Journal of Psychosomatic Research , 101, 24–30.

Forman, J.P., Stampfer, M.J. & Curhan, G.C. (2009). Diet and lifestyle risk factors associated with incident hypertension in women . JAMA: Journal of the American Medical Association , 302(4), 401–411.

Suarez, E. (2002). Prognosis and outcome of first-episode psychoses in Hawai’i: Results of the 15-year follow-up of the Honolulu cohort of the WHO international study of schizophrenia . ProQuest Information & Learning, Dissertation Abstracts International: Section B: The Sciences and Engineering , 63(3-B), 1577.

Young, J.T., Heffernan, E., Borschmann, R., Ogloff, J.R.P., Spittal, M.J., Kouyoumdjian, F.G., Preen, D.B., Butler, A., Brophy, L., Crilly, J. & Kinner, S.A. (2018). Dual diagnosis of mental illness and substance use disorder and injury in adults recently released from prison: a prospective cohort study . The Lancet. Public Health , 3(5), e237–e248.

References and Further Reading

Greenhalgh, T. (2014). How to Read a Paper : The Basics of Evidence-Based Medicine , John Wiley & Sons, Incorporated, Somerset, United Kingdom.

Hoffmann, T. a., Bennett, S. P., & Mar, C. D. (2017). Evidence-Based Practice Across the Health Professions (Third edition. ed.): Elsevier.

Song, J.W. & Chung, K.C. (2010). Observational studies: cohort and case-control studies . Plastic and Reconstructive Surgery , 126(6), 2234-42.

Mann, C.J. (2003). Observational research methods. Research design II: cohort, cross sectional, and case-control studies . Emergency Medicine Journal , 20(1), 54-60.

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• Next: Randomised Controlled Trial >>
• Last Updated: Jun 13, 2024 10:34 AM
• URL: https://deakin.libguides.com/quantitative-study-designs

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How to choose your study design

Affiliation.

• 1 Department of Medicine, Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
• PMID: 32479703
• DOI: 10.1111/jpc.14929

Research designs are broadly divided into observational studies (i.e. cross-sectional; case-control and cohort studies) and experimental studies (randomised control trials, RCTs). Each design has a specific role, and each has both advantages and disadvantages. Moreover, while the typical RCT is a parallel group design, there are now many variants to consider. It is important that both researchers and paediatricians are aware of the role of each study design, their respective pros and cons, and the inherent risk of bias with each design. While there are numerous quantitative study designs available to researchers, the final choice is dictated by two key factors. First, by the specific research question. That is, if the question is one of 'prevalence' (disease burden) then the ideal is a cross-sectional study; if it is a question of 'harm' - a case-control study; prognosis - a cohort and therapy - a RCT. Second, by what resources are available to you. This includes budget, time, feasibility re-patient numbers and research expertise. All these factors will severely limit the choice. While paediatricians would like to see more RCTs, these require a huge amount of resources, and in many situations will be unethical (e.g. potentially harmful intervention) or impractical (e.g. rare diseases). This paper gives a brief overview of the common study types, and for those embarking on such studies you will need far more comprehensive, detailed sources of information.

Keywords: experimental studies; observational studies; research method.

© 2020 Paediatrics and Child Health Division (The Royal Australasian College of Physicians).

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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

• Describes specific characteristics in a population of interest
• The most common forms are case reports and case series
• In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
• In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

• This design is transverse where we take a specific sample at a specific time without any follow-up
• It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
• This design is easy to conduct
• For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

• We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
• It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
• Prospective : we follow the individuals in the future to know who will develop the disease
• Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
• This design is the strongest among the observational studies
• For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

• We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
• This design is always retrospective
•  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
•  Relatively easy to conduct
• For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

• Also known as interventional studies
• Can involve animals and humans
• Pre-clinical trials involve animals
• Clinical trials are experimental studies involving humans
• In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

• In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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A prospective study, sometimes called a prospective cohort study, is a type of longitudinal study where researchers will follow and observe a group of subjects over a period of time to gather information and record the development of outcomes.

The participants in a prospective study are selected based on specific criteria and are often free from the outcome of interest at the beginning of the study. Data on exposures and potential confounding factors are collected at regular intervals throughout the study period.

By following the participants prospectively, researchers can establish a temporal relationship between exposures and outcomes, providing valuable insights into the causality of the observed associations.

This study design allows for the examination of multiple outcomes and the investigation of various exposure levels, contributing to a comprehensive understanding of the factors influencing health and disease.

How it Works

Participants are enrolled in the study before they develop the outcome or disease in question and then are observed as it evolves to see who develops the outcome and who does not.

Cohort studies are observational, so researchers will follow the subjects without manipulating any variables or interfering with their environment.

Similar to retrospective studies , prospective studies are beneficial for medical researchers, specifically in the field of epidemiology, as scientists can watch the development of a disease and compare the risk factors among subjects.

Before any appearance of the disease is investigated, medical professionals will identify a cohort, observe the target participants over time, and collect data at regular intervals.

Weeks, months, or years later, depending on the duration of the study design, the researchers will examine any factors that differed between the individuals who developed the condition and those who did not.

They can then determine if an association exists between an exposure and an outcome and even identify disease progression and relative risk.

Determine cause-and-effect relationships

Because researchers study groups of people before they develop an illness, they can discover potential cause-and-effect relationships between certain behaviors and the development of a disease.

Multiple diseases and conditions can be studied at the same time

Prospective cohort studies enable researchers to study causes of disease and identify multiple risk factors associated with a single exposure. These studies can also reveal links between diseases and risk factors.

Can measure a continuously changing relationship between exposure and outcome

Because prospective cohort studies are longitudinal, researchers can study changes in levels of exposure over time and any changes in outcome, providing a deeper understanding of the dynamic relationship between exposure and outcome.

Limitations

Time consuming and expensive.

Prospective studies usually require multiple months or years before researchers can identify a disease’s causes or discover significant results.

Because of this, they are often more expensive than other types of studies. Recruiting and enrolling participants is another added cost and time commitment.

Requires large subject pool

Prospective cohort studies require large sample sizes in order for any relationships or patterns to be meaningful. Researchers are unable to generate results if there is not enough data.

• Framingham Heart Study: Studied the effects of diet, exercise, and medications on the development of hypertensive or arteriosclerotic cardiovascular disease in residents of the city of Framingham, Massachusetts.
• Caerphilly Heart Disease Study: Examined relationships between a wide range of social, lifestyle, dietary, and other factors with incident vascular disease.
• The Million Women Study: Analyzed data from more than one million women aged 50 and over to understand the effects of hormone replacement therapy use on women’s health.
• Nurses’ Health Study: Studied the effects of diet, exercise, and medications on the development of hypertensive or arteriosclerotic cardiovascular disease.
• Sleep-Disordered Breathing and Mortality: Determined whether sleep-disordered breathing and its sequelae of intermittent hypoxemia and recurrent arousals are associated with mortality in a community sample of adults aged 40 years or older (Punjabi et al., 2009)

Frequently Asked Questions

1. what does it mean when an observational study is​ prospective.

A prospective observational study is a type of research where investigators select a group of subjects and observe them over a certain period.

The researchers collect data on the subjects’ exposure to certain risk factors or interventions and then track the outcomes. This type of study is often used to study the effects of suspected risk factors that cannot be controlled experimentally.

2. What is the primary difference between a randomized clinical trial and a prospective cohort study?

In a retrospective study, the subjects have already experienced the outcome of interest or developed the disease before the start of the study.

The researchers then look back in time to identify a cohort of subjects before they had developed the disease and use existing data, such as medical records, to discover any patterns.

In a prospective study, on the other hand, the investigators will design the study, recruit subjects, and collect baseline data on all subjects before any of them have developed the outcomes of interest.

The subjects are followed and observed over a period of time to gather information and record the development of outcomes.

3. What is the primary difference between a randomized clinical trial and a prospective cohort study?

In randomized clinical trials , the researchers control the experiment, whereas prospective cohort studies are purely observational, so researchers will observe subjects without manipulating any variables or interfering with their environment.

Researchers in randomized clinical trials will randomly divide participants into groups, either an experimental group or a control group.

However, in prospective cohort studies, researchers will identify a cohort and observe the target participants as a whole to examine any factors that differ between the individuals who develop the condition and those who do not.

Euser, A. M., Zoccali, C., Jager, K. J., & Dekker, F. W. (2009). Cohort studies: prospective versus retrospective. Nephron. Clinical practice, 113(3), c214–c217. https://doi.org/10.1159/000235241

Hariton, E., & Locascio, J. J. (2018). Randomised controlled trials – the gold standard for effectiveness research: Study design: randomised controlled trials. BJOG : an international journal of obstetrics and gynaecology, 125(13), 1716. https://doi.org/10.1111/1471-0528.15199

Netherlands Cooperative Study on the Adequacy of Dialysis-2 Study Group de Mutsert Renée r. de_mutsert@ lumc. nl Grootendorst Diana C Boeschoten Elisabeth W Brandts Hans van Manen Jeannette G Krediet Raymond T Dekker Friedo W. (2009). Subjective global assessment of nutritional status is strongly associated with mortality in chronic dialysis patients. The American journal of clinical nutrition, 89(3), 787-793.

Punjabi, N. M., Caffo, B. S., Goodwin, J. L., Gottlieb, D. J., Newman, A. B., O”Connor, G. T., Rapoport, D. M., Redline, S., Resnick, H. E., Robbins, J. A., Shahar, E., Unruh, M. L., & Samet, J. M. (2009). Sleep-disordered breathing and mortality: a prospective cohort study. PLoS medicine, 6(8), e1000132. https://doi.org/10.1371/journal.pmed.1000132

Ranganathan, P., & Aggarwal, R. (2018). Study designs: Part 1 – An overview and classification. Perspectives in clinical research, 9(4), 184–186.

Song, J. W., & Chung, K. C. (2010). Observational studies: cohort and case-control studies. Plastic and reconstructive surgery, 126(6), 2234–2242. https://doi.org/10.1097/PRS.0b013e3181f44abc.

Further Information

• Euser, A. M., Zoccali, C., Jager, K. J., & Dekker, F. W. (2009). Cohort studies: prospective versus retrospective. Nephron Clinical Practice, 113(3), c214-c217.
• Design of Prospective Studies
• Hammoudeh, S., Gadelhaq, W., & Janahi, I. (2018). Prospective cohort studies in medical research (pp. 11-28). IntechOpen.
• Nabi, H., Kivimaki, M., De Vogli, R., Marmot, M. G., & Singh-Manoux, A. (2008). Positive and negative affect and risk of coronary heart disease: Whitehall II prospective cohort study. Bmj, 337.
• Bramsen, I., Dirkzwager, A. J., & Van der Ploeg, H. M. (2000). Predeployment personality traits and exposure to trauma as predictors of posttraumatic stress symptoms: A prospective study of former peacekeepers. American Journal of Psychiatry, 157(7), 1115-1119.

Power of Cohorts: Public Health Advances from Prospective Cohort Studies

June 24, 2024 , by Jennifer K. Loukissas, M.P.P.

DCEG's Commitment to Collaboration

Etiologic discovery of the causes of cancer and other chronic diseases depends upon the power of the prospective cohort study. The intermingled factors that influence risk—heredity, environment, occupation, lifestyle—are challenging enough to tease apart without the limitations of the other primary approach, case-control studies. While swift to produce results, case-control studies offer limited insight and have been documented to produce the wrong results for many exposures.

The multi- and inter-disciplinary, collaborative approaches that cohort studies require are the hallmark of DCEG research. Teams of epidemiologists, geneticists, biostatisticians, and other experts, employ various tools to uncover the causes of cancer.

As part of the intramural research program at the National Cancer Institute, DCEG is a natural incubator for high-risk, high-reward, time-intensive projects, such as cohort studies, that depend on stable, long-term funding. Collaborations are key to their success. Partnerships across the Division and with extramural investigators across the country and around the world have expanded exponentially the value of these resources.

Among the tremendous discoveries and significant public health advances to come from such undertakings are the benefits of exercise for cancer prevention and the association of various exposures to elevated cancer risk, including the determination that smoking causes lung cancer. Cohort studies have informed recommendations like those in Healthy People 2030 ; regulatory guidelines for population-level exposures to potential or known carcinogens ; safety procedures in the workplace; programs to prevent infections and chronic disease; and clinical management following a cancer diagnosis.

The length of longitudinal studies, which may continue for 20 to 30 years, allows researchers to track changes in exposures, lifestyle, or health status over time. Participants contribute maximally when they remain active in the study for decades, providing detailed information repeatedly, from various sources, such as lengthy questionnaires, blood samples, linkage to wearable digital devices, and clinic or home visits for collection of biological samples, like urine. Future studies have plans to collect stool, which will be valuable for examining the microbiome and other metabolic factors.

Physical Activity Associated with Reduced Risk of Seven Cancers

Recommendations for physical activity in US Guidelines is associated with reduced risk of seven cancers.

Participant samples become time capsules. Vials of frozen material stored in biobanks increase in value as the years go by until a future investigator with a novel assay discovers biomarkers unimaginable at the time of collection. For example, ‘omics’ technologies in use today are being applied to data and biological samples collected a generation ago.

The following is an overview of some U.S.-based longitudinal cohort studies utilized and maintained by investigators in DCEG and news about two new, exciting, modern cohorts. Many of these studies have pooled their data as part of the NCI Cohort Consortium and other consortia.

General Population Cohorts Inform Population-Level Prevention

Historically, cancer has been a disease of aging. As such, most cohorts enroll individuals in mid-life or later. Two of the most celebrated —launched in the 1990s—are the NIH-AARP Diet and Health Study, and PLCO, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Study. In addition to the wealth of knowledge generated by DCEG investigators, the broader scientific community can access data and biospecimens from these studies for their own investigations. Details on accessing that information follow each study description. 

NIH-AARP Diet and Health Study

NIH-AARP Diet and Health Study Reaches Milestone

Nearly 25 years ago, the NCI launched the largest prospective in-depth study on diet and health.

The NIH-AARP Diet and Health Study  recruited participants from the membership rolls of AARP, formerly the American Association of Retired Persons, to amass what was then the largest cohort study in the world. Thirty years on, data collected from those half-million individuals are still being analyzed and new findings continue to improve our understanding of patterns of behavior in mid-life and their effect on future cancer risk.

Detailed information from multiple questionnaires has enabled over 900 project proposals resulting in over 600 publications. Using dietary information, investigators in the Metabolic Epidemiology Branch , along with colleagues, have observed many important patterns, such as the safety of coffee consumption—even at five or six cups per day . Other critical observations from this cohort: there is no safe level of exposure to tobacco smoke; even low-intensity smokers benefit from cessation . By mapping the residential histories provided by study participants to air pollution data, investigators in the Occupational and Environmental Epidemiology Branch (OEEB)  linked elevated levels of ultrafine particulate exposure  with increased risk of adenocarcinoma of the lung. Similarly, high levels of fine particulate air pollution were associated with increased breast cancer incidence.  The effort to map participant residences also led to the important observation of an   association between industrial emissions of ethylene oxide and  in situ  breast cancer . A similar pattern was described for ambient dioxin emissions and the risk of non-Hodgkin lymphoma . These studies demonstrate the power of residence history mapping (i.e., geocoding), an important add-on to the cohort. 

Learn more about the NIH-AARP Diet and Health Study and see a summary of select findings .

Researchers interesting in  accessing data can use the NIH-AARP STARS portal to learn about the process and submit their proposals.

PLCO: A Screening Trial that Became a Prospective Cohort

The Prostate, Lung, Colorectal, and Ovarian Cancer Screening Study (PLCO) Cohort began as a screening trial. DCEG investigators and colleagues from NCI and the 10 participating centers transformed it into a large observational cohort study that is still producing critical findings today.

Perfluorooctanoic Acid (PFOA) Associated with Increased Risk of Kidney Cancer

Jonathan Hofmann and collaborators evaluate serum levels of perfluorooctanoic acid (PFOA) in a prospective population-based U.S. cohort.

Survey data and serial biological samples allowed for over 2,000 projects resulting in over 1,400 publications, including the identification of novel biomarkers. For example, investigators in the Infections and Immunoepidemiology Branch (IIB) discovered that human papillomavirus (HPV) antibodies in the blood could be used to predict risk of HPV-positive oropharyngeal cancer years before diagnosis .

In the 2020s, DCEG investigators completed whole genome sequencing of the PLCO samples and made available to the publics summary statistics through The PLCO Atlas – GWAS Explorer .

Investigators seeking access to complete cancer incidence, mortality data, and biospecimens for each subject in the PLCO trial can enter those requests online.

A Modern Cohort: Connect for Cancer Prevention

Exposures and lifestyles change with time among individuals and at the population level. To protect the health of today's generation and prevent the cancers of the future, epidemiologists must embark upon the construction of new cohorts. Beginning in the mid-2010s, DCEG investigators began planning the Connect for Cancer Prevention Study . To sow the seeds of future research discoveries, they are recruiting Gen X and Millennials whose lifestyles include entirely novel practices and experiences, compared to their parents of the Silent and Baby Boom generations.

Connect began enrollment in 2021 and as of June 2024, surpassed 40,000 participants. The aims is to enroll 200,000 adults between the ages of 30 and 70, who have not previously been diagnosed with cancer, and who receive their health care from one of 10 partner health care systems. With this latter criterion, participants can readily share access to their electronic medical records (EMRs)—a component missing from the general population cohorts described above. Furthermore, EMR integration will aid with long-term follow up and increase the completeness of the participant data.

Consented participants in Connect complete extensive, online questionnaires and biospecimen collections—blood, urine, and saliva—at enrollment and periodically throughout the duration of follow-up. Over the course of the study, tissue collected from biopsies and invasive cancers will also be shared with Connect investigators for molecular studies. Passive follow-up via tumor registries, the National Death Index, and EMRs will provide additional outcome information for cancers and their precursors.

Connect is a digital-first cohort, built with a Findable, Accessible, Interoperable, and Reproducible (FAIR) data infrastructure that allows for sharing and collaboration on scales legacy cohorts could not achieve. This state-of-the-art cohort is built with an efficient, flexible, and integrated data infrastructure that makes the most of modern interoperability standards to serve as a research resource for future generations of scientists at the NCI and across the broader scientific community.

Update: Connect for Cancer Prevention Study

Latest news and accomplishments: recruitment & retention, biospecimen collection, and more

Additionally, by incorporating a diverse Participant Advisory Board and partnering with health systems that serve diverse communities, Connect can enhance recruitment of populations typically underrepresented in research. While there are several studies in the U.S. that have sought to address these gaps, including the Southern Community Cohort, the Multiethnic Cohort, and the Black Women’s Health Study, historically, most cohorts recruited from a relatively narrow segment of the population—predominately White, cis-gender, well-educated, higher-income individuals—limiting the generalizability of the findings.

Learn more about Connect on the GitHub site.

Browse the Connect for Cancer Prevention Study participant recruitment website.

Exposure-based Cohorts

DCEG also prioritizes research in populations with unique exposures, such as workers exposed in occupational settings, or individuals with unique health conditions or medical exposures. The discoveries from such investigations benefit not only the populations studied but also the general population, which may experience similar exposures, though typically at lower rates or doses. 

Cohorts to Study the Health of Workers

Experts in OEEB and the Radiation Epidemiology Branch (REB) have studied worker populations for over 40 years. These cohorts provided some of the earliest data on the potential harms from industrial chemicals and ionizing radiation.

Industry and Manufacturing: Formaldehyde, Diesel Exhaust, and Acrylonitrile

Occupational Formaldehyde Exposure and Cancer Risk

Studies informed classification of formaldehyde as a human carcinogen

Workers whose jobs involve the use of toxic chemicals and other potentially harmful substances are often exposed at levels well above those of the general public. With well-designed questionnaires, reliable exposure assessment, careful participant recruitment, and long-term follow up, occupational cohort studies can profoundly influence safety in the workplace and regulations to protect public health.

For example, OEEB investigators have conducted countless studies resulting in important discoveries, from dry cleaners exposed to solvents to workers whose jobs involved exposure to formaldehyde . Data from these cohorts informed the classification of those exposures as carcinogenic to humans by the International Agency for Research on Cancer (IARC) Monograph Programme and the National Toxicology Program Report on Carcinogens.

The Diesel Exhaust in Miners Study (DEMS) , launched in 1992 in collaboration with the National Institute for Occupational Safety and Health, enrolled workers at eight non-metal mines across the country. DEMS captured comprehensive exposure and lifestyle data, which allowed the investigators to clarify the relationship between exposure to diesel engine exhaust and the risk of death from lung cancer. The findings played a critical role in the classification of diesel exhaust as a Group 1 carcinogen by IARC in 2012  and have important implications for miners, tens of millions of workers in the U.S. and worldwide who are exposed to diesel exhaust in the workplace, and people who live in cities with high levels of diesel exhaust.

Diesel Exhaust in Miners Study II Reveals New Insights

Extended follow-up shows elevated lung cancer risk remained 20 or more years after diesel exhaust exposure ceased.

Acrylonitrile is a chemical used in the production of synthetic fibers and many other products. While results from animal bioassays suggested it might cause cancers at multiple sites, findings from early epidemiologic studies were inconsistent and inconclusive due to small sample size and poor exposure characterization. The NCI Acrylonitrile Cohort , the largest to date, found workers with the highest cumulative exposure experienced excess lung cancer more than 20 years after first exposure. An additional 21 years of mortality data showed an exposure-response relationship for lung cancer death and positive associations for death from bladder cancer and for non-malignant respiratory disease. IARC’s Monograph Programme evaluated this exposure in June 2024.

Farmers and Pesticide Applicators

The Agricultural Health Study (AHS) works to understand how agricultural, lifestyle, and genetic factors affect the health of farming populations. Since its inception, AHS investigators have evaluated agricultural practices and pesticide use, other occupational exposures, and a broad range of factors as they relate to risk for cancer and other outcomes. Data from the AHS have contributed to determinations of carcinogenicity of agricultural exposures as well as regulatory decisions in the U.S. and internationally.

Biomarkers of Exposure and Effect in Agriculture

BEEA investigates biological mechanisms of pesticides and cancer risk.

More recently, DCEG investigators have led a molecular epidemiologic initiative known as the Biomarkers of Exposure and Effect in Agriculture (BEEA) study. Within BEEA, biospecimens and updated exposure information are being used to investigate the biologic mechanisms underlying associations between agricultural exposures and risk of cancer and other chronic diseases.

More information about AHS and BEEA can be found on the AHS website.

Medical Radiation Workers

The U.S. Radiologic Technologists Study (USRT or Rad Tech) has expanded our understanding of the radiation-related health effects for medical workers who administer diagnostic and therapeutic medical exams. This nationwide study began in 1982 with more than 110,000 current and former radiologic technologists, certified by the American Registry of Radiologic Technologists, who completed one or more questionnaires about their work history, health status, and other factors.

The Rad Tech Study has yielded important findings related to health risks from repeated exposure to relatively low doses of ionizing radiation, including associations between cumulative lifetime radiation exposure and risks of female breast cancer , lung cancer, and cataracts. Additional analysis demonstrated that cataract risk was particularly high for technologists who were positioned closer to the radiation source  while risk was much lower for those who used personal protection equipment (room shields, lead glasses). The cohort has also been a valuable resource for investigating the effects of ultraviolet light exposure and other lifestyle factors on cancer and other health outcomes .

Individuals with Specific Medical Exposures or Diagnoses

The DES Story: Lessons Learned

Dr. Robert Hoover discusses a followup study of diethylstilbestrol (DES), a drug once prescribed to pregnant women. (Video produced and edited by Natalie Giannosa)

Multi-Generation Study of DES-Exposed Individuals

From the mid-1940s through the early 1970s, diethylstilbestrol (DES)—the first synthetic estrogen—was given to millions of pregnant women, exposing daughters and sons while in utero . It was thought to prevent miscarriage. Instead, DES was later identified as a human carcinogen and the first known trans-placental carcinogen.

In 1971, the first study was published connecting a mother’s prescription for DES during pregnancy and the occurrence of vaginal cancer in her daughter, prompting the FDA to revoke the use of DES in pregnant women. Several field studies were launched across the country. In 1992, NCI investigators and collaborators brought together those individual study centers to create the NCI Follow-up of Combined DES Cohorts . With the greater statistical power of the combined studies, investigators identified a constellation of adverse health outcomes in three generations , including an increased frequency of problems of the reproductive tract, changes in the tissue of the vagina, infertility, and poor pregnancy outcomes in daughters. As DES-exposed offspring reach the age when cancer rates begin to rise, it is important to continue to monitor the long-term risk of cancer and other adverse health outcomes in this unique population.

Some Breast Cancer Treatments Linked to Long-term Cardiovascular Disease Risk

This study may inform long-term and age-specific cardiovascular disease follow-up for breast cancer survivors.

Cancer Survivors

Over 18 million Americans are survivors of one or more cancers. Survivors of cancer are at risk for a second primary malignancy either because of their exposures in life, genetic predisposition, or adverse effects of their treatment.

To investigate these risks, investigators in REB and the Integrative Tumor Epidemiology Branch convened a retrospective record-linkage cohort, the Kaiser Permanente (KP) Breast Cancer Survivors Cohort , a transdisciplinary resource to investigate treatment patterns over time and the risk of second cancers, cardiovascular disease, and mortality.

Among their findings to date, they observed breast cancer patients who received radiotherapy, had breast-conserving surgery, and had a history of hypertension or diabetes at the time of their breast cancer diagnosis had elevated risks for thoracic angiosarcoma .

A New Cohort of Children Treated for Cancer

As therapies to treat cancer continue to evolve, it is important to monitor short- and long-term adverse health outcomes. The Pediatric Proton and Photon Therapy Comparison Cohort , supported by the Childhood Cancer Data Initiative since 2020, is a multi-center retrospective cohort to compare the risk of second cancers among childhood cancer patients treated with proton radiotherapy to those treated with photon radiotherapy.

Pediatric Proton and Photon Therapy Comparison Cohort

A comparison of second cancer risk following proton versus photon therapy for pediatric cancer.

Investigators in REB and collaborators from Massachusetts General Hospital are assembling patient and radiotherapy treatment data from participating study centers across the United States and Canada. REB experts are developing state-of-the-art dosimetry methods to quantify radiation doses to exposed organs and tissues. Investigators will examine dose-response and assessment of dose-volume effects for the most common and radiosensitive second cancer sites (brain tumors, sarcomas, breast and thyroid cancer). The study is expected to continue for decades in order to capture the range of the late effects that may be associated with these therapies.

International Cohorts

The encyclopedic breadth of research within and across cohort studies in the Division could not begin to fit in the length of this article; the focus here was limited to projects in the United States. In collaboration with international partners, we have assembled many cohorts of truly unique populations. For example, the Shanghai Women’s Health Study , in collaboration with Vanderbilt University and the Shanghai Cancer Center, is a population-based prospective cohort of about 75,000 mostly never-smoking women recruited between 1997 and 2000 with blood and urine sample collection and followed via multiple in-person surveys and record linkages with population-based registries.

In Costa Rica, where DCEG has been studying cervical cancer for over 40 years, the Guanacaste HPV Natural History Study has followed over 10,000 women since 1993. It has yielded many critical insights into HPV natural history, including the evidence to establish the performance of then-novel HPV and cytologic screening techniques.

Collaborations in Ukraine have advanced our understanding of the health effects of low-dose exposure to ionizing radiation . Among a cohort of individuals exposed to radioactive fallout following the accident at the Chernobyl power plant, investigators have quantified the relationship between internal exposure to radiation in childhood and thyroid cancer detected through standardized screening procedures.

The Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study , conducted in southwestern Finland, has been an integral research resource for NCI for over three decades. It was designed to test nutritional approaches to cancer prevention and the biological and anti-neoplastic properties of two antioxidants micronutrients , beta-carotene and vitamin E, among nearly 30,000 male smokers.

See an inventory of cohorts in DCEG on our website.

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May 5-8, 2024

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Overall Retention and Characteristics Associated With Longitudinal Completion of Remote Patient-Reported Outcome Questionnaires in a Representative, General Population Cohort Study: The Project Baseline Health Study

Carroll MK 1 , Faheem S 1 , Bouteiller J 1 , Hernandez AF 2 , Mahaffey K 3 , Mega JL 1 , Pagidipati N 2 , Schaack T 4 , Shah SH 2 , Shashidhar S 3 , Swope S 3 , Williams D 3 , Plowman RS 1 , Simard EP 1 , Short SA 1 , Sullivan SS 3 1 Verily Life Sciences, South San Francisco, CA, USA, 2 Duke University, Durham, NC, USA, 3 Stanford University, Stanford, CA, USA, 4 California Health & Longevity Institute, Westlake Village, CA, USA

Presentation Documents

• ISPOR24_Carroll_PCR88_POSTER136784.pdf

OBJECTIVES: Remote, digitally-supported longitudinal cohort studies are promising for research due to their potential for participant-centered, convenient, cost-efficient, scalable, and secure data collection that may increase participation and representativeness. However, it is well-recognized that longitudinal compliance is challenging for remote studies. The Project Baseline Health Study (PBHS), a hybrid in-person and virtual study, offers a unique opportunity to evaluate aspects of remote engagement over time.

METHODS: We summarized overall four-year retention in PBHS and rates of longitudinal remote survey completion. We also investigated associations of demographic, social determinants of health, physical, and mental health characteristics with quarterly remote survey completion using regression models.

RESULTS: A total of 2502 participants enrolled in PBHS; 94% remained enrolled after 4 years and 60% of participants completed all 4 annual follow-up visits . There were 2490 participants enrolled in PBHS for at least one quarter. The median (IQR) number of remote electronic survey sets completed was 8 (3-12) of a possible 16. Age was positively associated with remote survey completion. Black (OR: 0.70; 95% CI: 0.59-0.84) and Hispanic (OR: 0.75; 95% CI: 0.62-0.92) participants had lower odds of completion versus White and non-Hispanic counterparts, respectively. Income and education were positively associated with remote survey completion. Those with at least mild symptoms of depression (OR: 0.87; 95% CI: 0.81-0.93) or anxiety (OR: 0.83; 95% CI: 0.77-0.89), reported via 9-item Participant Health Questionnaire and 7-item Generalized Anxiety Disorder questionnaire, respectively, had lower odds of remote survey completion versus those without.

Patient-Centered Research, Study Approaches

Topic Subcategory

Adherence, Persistence, & Compliance, Patient Engagement, Prospective Observational Studies

Mental Health (including addition)

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Assessing the uptake of incentivised physical health checks for people with serious mental illness: a cohort study in primary care

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• Figures & Data

Background People with serious mental illness are more likely to experience physical illnesses. The onset of many of these illnesses can be prevented if detected early. Physical health screening for people with serious mental illness is incentivised in primary care in England through the Quality and Outcomes Framework (QOF). GPs are paid to conduct annual physical health checks on patients with serious mental illness, including checks of body mass index (BMI), cholesterol, and alcohol consumption.

Aim To assess the impact of removing and reintroducing QOF financial incentives on uptake of three physical health checks (BMI, cholesterol, and alcohol consumption) for patients with serious mental illness.

Design and setting Cohort study using UK primary care data from the Clinical Practice Research Datalink between April 2011 and March 2020.

Method A difference-in-difference analysis was employed to compare differences in the uptake of physical health checks before and after the intervention, accounting for relevant observed and unobserved confounders.

Results An immediate change was found in uptake after physical health checks were removed from, and after they were added back to, the QOF list. For BMI, cholesterol, and alcohol checks, the overall impact of removal was a reduction in uptake of 14.3, 6.8, and 11.9 percentage points, respectively. The reintroduction of BMI screening in the QOF increased the uptake by 10.2 percentage points.

Conclusion This analysis supports the hypothesis that QOF incentives lead to better uptake of physical health checks.

• physical health checks
• primary health care
• mental illness
• cohort studies
• Introduction

Pay-for-performance is a healthcare reimbursement model that provides financial incentives to healthcare providers to deliver high-quality care and achieve specific performance targets. Pay-for-performance schemes have been implemented in many countries around the world including the US, the UK, Australia, Canada, and Germany, and across a range of healthcare settings, 1 including to incentivise preventive activities in primary care. 2 However, their effectiveness is unclear. 3

In the UK, the Quality and Outcomes Framework (QOF) is a pay-for-performance scheme that incentivises GPs to meet specific quality targets across different aspects of healthcare delivery, such as management and treatment of specific chronic conditions, preventive care, and patient safety. In 2006, GPs were incentivised to conduct annual physical health checks on patients with serious mental illness. These patients are at increased risk of physical ill health, 4 , 5 and their life expectancy is around 20 years lower than for the general population. 6 – 8 Most premature deaths in this population are attributable to preventable causes. 9 The rationale for incentivising physical health monitoring is that proactive physical reviews lead to earlier detection of physical health problems and trigger appropriate interventions that can prevent deterioration of health issues, and improve health outcomes. This can result in improvements in life expectancy and quality of life, and reductions in hospitalisations and costs. 10 Medical guidelines published in the US, Australia, Brazil, Canada, and Europe have recommended physical health checks for people with serious mental illness, and some of them explicitly involve GPs in this responsibility. 11 , 12

In the UK, >535 000 people with serious mental illness are registered with a GP, 13 who oversees care, prescribes medication, and provides both mental and physical health care. In the English NHS, primary care has the lead responsibility for carrying out annual physical health checks and follow-up care for patients with serious mental illness who are not in contact with or only recently established contact with secondary mental health services, and/or whose condition has stabilised. 14 , 15 Increasing the proportion of people with a comprehensive physical health review conducted in primary care is a high policy priority. 16 – 18 The annual physical health checks incentivised by the QOF include checks on alcohol consumption, blood pressure (BP), cholesterol, body mass index (BMI), and blood glucose. However, not all of these checks were incentivised continuously (see Table 1 ). In the financial year 2014–2015 (running from 1 April 2014 to 31 March 2015), BMI, cholesterol, and blood glucose checks were removed from the QOF. In the financial year 2019–2020, BMI screening was re-introduced but alcohol consumption screening was removed. In financial year 2021–2022, all indicators were included in the QOF.

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Development of physical health checks over time

There is limited evidence about the effects of removing incentivised activities from pay-for-performance programmes on care delivery. A US study 19 analysed the impact of removing financial incentives from four clinical quality indicators in Kaiser Permanente, an integrated healthcare delivery system providing comprehensive medical care to about 3.1 million people in northern California. The removal of incentives was associated with a decrease in screening rates. In the UK, research has studied the impact of removing physical health checks from the QOF on uptake using practice-level data. 20 The authors found an immediate reduction in performance on quality measures.

However, none of these studies analyse the effect of the reintroduction of financial incentives on the uptake of physical health checks. The aim of this study was to assess the impact of removing and then reintroducing financial incentives for three physical health checks (BMI, cholesterol, and alcohol consumption) on their uptake for patients with serious mental illness. To the authors’ knowledge, this is the first study that uses patient-level data to analyse the impact of financial incentives on physical health check uptake, enabling more robust inferences on their association.

How this fits in

We used de-identified patient electronic health records from the Clinical Practice Research Datalink (CPRD) GOLD database, which collects fully coded routine care data from a network of GP practices using the same software system (Vision). Patients in CPRD GOLD are broadly representative of the English general population in terms of age, sex, ethnicity, and BMI. 21 However, areas in the East Midlands, Yorkshire and the Humber, and the North East of England are under-represented in the data.

We considered all patients aged ≥18 years who were registered with a practice in CPRD GOLD at any time between April 2011 and March 2020, and had a diagnosis of schizophrenia, bipolar disorder, and other psychoses and other affective disorders documented in primary care. Although the QOF serious mental illness register is defined based on the first three diagnosis types, 22 we included other affective disorders (constituting <4% of all serious mental illness diagnosis in our cohort) in the definition of the population with serious mental illness to broaden the scope of the QOF policy evaluation on the wider population with serious mental illness. We followed the NHS England technical guidance 2019/2020 17 to identify GP-led physical health checks.

The primary outcome was a binary variable indicating whether a patient received a physical health check in a given year.

The exposure variable (hereafter referred to as intervention) is an indicator for whether the physical health check was subject to the policy change, that is, the removal from or reintroduction to QOF.

We used several covariates at a patient level: age, sex (male/female), ethnicity, years since serious mental illness diagnosis, type of serious mental illness disorder (schizophrenia, bipolar disorder, and/or other psychoses and other affective disorders), deprivation, and comorbidities.

As a measure of deprivation, we used quintiles of the deprivation level associated with a patient’s area of residence as captured by the 2015 English Index of Multiple Deprivation. 23

Morbidities, identified using Read codes (standard clinical coding used in UK general practice), 24 were included as binary variables indicating whether a condition had been recorded by the time patients entered each cohort. We included the following 12 conditions: asthma, atrial fibrillation, cancer, coronary heart disease, chronic kidney disease, chronic obstructive pulmonary disease, diabetes, heart failure, hypertension, chronic liver disease, rheumatoid arthritis, and stroke or transient ischaemic attack.

Statistical analysis

To assess the impact of the removal and reintroduction of financial incentives for physical health checks on their uptake, we calculated the annual uptake rate of each physical health check in the eligible patient population registered with CPRD practices in each financial year between 2011–2012 and 2019–2020. Patients who had the same physical health check more than once in a financial year were only counted once.

We conducted a case-control study where we compared physical health checks that were incentivised for only some of the study period (cases) with physical health checks that remained incentivised throughout (controls). We employed a difference-in-differences analysis to compare differences in the uptake before and after the intervention between cases and controls. The main variables of interest were the interaction terms (formed by multiplying the exposure with time indicators), which capture the uptake changes over time of the cases relative to the controls.

The rationale for the difference-in-differences approach was that both cases and controls would be subject to the same external influences and thus exhibit similar pre-intervention time trends. Any differences in physical health check uptake observed in cases over and above the time trend of controls would therefore be attributable to the intervention (policy change).

The following QOF policy changes were analysed: the removal of BMI and cholesterol checks from the QOF in 2014–2015; the reintroduction of BMI checks in the QOF in the financial year 2019–2020; and the removal of alcohol checks from the QOF in 2019–2020. The analysis of the financial year 2014–2015 policy changes was performed in a cohort of 5635 patients (cohort-2012) who remained continuously enrolled in the same practice 2 years before and 2 years after the removal of BMI and cholesterol checks from the QOF (that is, between 1 April 2012 and 31 March 2016). BP monitoring was incentivised throughout the study period and served as control. Each patient contributed with two records in any given year: one for the case physical health check and one for the control physical health check. The total number of observations in cohort-2012 was 45 080 (5635 patients x 4 years x 2 records). The analysis of the financial year 2019–2020 interventions was performed on a cohort of 3065 patients (cohort-2018) who remained continuously enrolled in the same practice 1 year before and 1 year after the reintroduction of BMI (that is, between 1 April 2018 and 31 March 2020). The total number of observations in cohort-2018 was 12 260 (3065 patients x 2 years x 2 records). We did not include blood glucose in the analysis since there was no control with a similar pre-intervention time trend.

We estimated linear probability regression models controlling for all the above-mentioned patient-level covariates and year fixed effects. All analyses were performed in Stata (version 17).

The two cohorts were similar in terms of patient characteristics (see descriptive statistics in Table 2 ).

Descriptive statistics: Cohort-2012 and Cohort-2018

Figure 1 shows the proportion of patients receiving BMI, cholesterol, BP, and alcohol consumption checks between financial years 2011–2012 and 2019–2020. The first vertical line indicates the financial year prior to the removal of BMI and cholesterol checks from the QOF in 2014–2015, whereas the second vertical line indicates the financial year prior to the reintroduction of BMI checks and the removal of alcohol checks in 2019–2020.

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Uptake of BMI, cholesterol, BP, blood glucose, and alcohol checks over time. The first vertical line indicates the financial year prior to the removal of BMI and cholesterol checks from the QOF in 2014–2015. The second vertical line indicates the financial year prior to the reintroduction of BMI checks and the removal of alcohol checks in 2019–2020. ALC = alcohol. BMI = body mass index. BP = blood pressure. CHOL = cholesterol. PHC = physical health check.

The proportion of patients receiving BMI checks dropped by 16 percentage points between 2013–2014 and 2014–2015 (from 66% in 2013–2014 to 50% in 2014–2015) and a further 2 percentage points (to 48%) in the subsequent year. This level remained constant until BMI checks were reintroduced in 2019–2020 when uptake increased to 57% ( Figure 1 ).

The time series for cholesterol checks display a similar pattern to that of BMI checks until 2019–2020, however, with lower levels of uptake continuing to reflect that cholesterol checks were reinstated as QOF indicators only in 2021–2022 ( Figure 1 ).

Alcohol check recording has remained stable from 2013–2014 to 2018–2019. After removal from the QOF in 2019–2020, a decrease in uptake from 54% in 2018–2019 to 41% in 2019–2020 is observed in Figure 1 .

BP monitoring, which has been consistently included in the QOF throughout the period 2011–2012 to 2019–2020, exhibits the highest uptake of all physical health checks (>70%). In most years, the uptake remained unchanged, with only a small variation observed around years where QOF policy changes occurred for other physical health checks, but not for BP ( Figure 1 ).

The level of BP, BMI, and cholesterol checks follow similar trends before the policy change (see Figure 1 ), a requirement for the difference-in-differences approach. Table 3 presents the estimates of the impact of removing BMI and cholesterol checks from the QOF in 2014–2015, using BP checks as a control group. The uptake of physical health checks might change over time irrespective of the introduction of the QOF policy, which is captured by the time indicators. These general temporal changes are all positive and significant, implying that the uptake of BP checks in all years is larger compared with the uptake of BP checks in 2012–2013.

Impact of removal of incentives in 2014–2015 on the uptake of BMI and cholesterol physical health checks

Differences in the uptake in the baseline year (2012–2013) between BMI/cholesterol and BP checks are given by the intervention coefficients. The uptake is lower for BMI and cholesterol checks compared with BP checks by 8.4 percentage points (95% confidence interval [CI] = −9.8 to −6.9) and 17.7 percentage points (95% CI = −18.6 to −16.7), respectively ( Table 3 ).

The impact of the policy change on the uptake of BMI/cholesterol checks after accounting for time trends and confounders amounts to −12.3 percentage points (95% CI = −14.2 to −10.4) and −6.8 percentage points (95% CI = −8.3 to −5.3) in the first year (difference-in-differences 2014–2015), respectively. The policy change reduced the uptake of BMI checks by another 2 percentage points in 2015–2016 but did not do so for cholesterol checks ( Table 3 ).

Table 4 presents the results of the 2019–2020 policy changes. Reintroducing BMI checks in the QOF was associated with a 10.2 percentage point increase in their uptake between 2018–2019 and 2019–2020. Removing alcohol physical health checks from the QOF list was associated with a reduction in their uptake by 11.9 percentage points.

Impact of reintroducing and removing incentives in 2019–2020 on the uptake of BMI and alcohol physical health checks

It appears that most of the morbidities included in the analysis appear to be associated with higher uptake of physical health checks (only diabetes is reported, others are available from the authors on request). Patients with diabetes are associated with the largest increases in physical health check uptake (for example, a 22 percentage point increase in the uptake of cholesterol versus BP physical health checks in cohort-2012, Table 3 ).

The introduction of the QOF pay-for-performance incentives aimed to increase the uptake of physical health checks in primary care. However, there is no consensus on how long incentives for quality indicators should remain in place. If achievement of these indicators reaches a ceiling, there is little room for further improvement. On the other hand, it is unclear whether incentivised behaviours become so ingrained that practices continue to perform well on the indicators if incentives are withdrawn. We explored the long-term impact of removing financial incentives on the uptake of physical health checks and we provided evidence on the impact of reintroducing quality indicators in the QOF.

Our analysis supports the hypothesis that QOF incentives affect the uptake of physical health checks. We find immediate changes in the uptake of BMI, cholesterol, and alcohol physical health checks following both their removal from and reintroduction to the QOF, that are consistent with the financial incentives set under the national pay-for-performance scheme. These changes are unlikely to be explained by other changes occurring in primary care around the same time of the QOF changes, as demonstrated by our difference-in-differences analysis. Our analysis also shows that patients with chronic physical conditions such as diabetes have high physical health check uptake. This is unsurprising as patients with these conditions are more closely monitored by GPs. For instance, physical health checks such as blood pressure, BMI, and cholesterol are also incentivised for patients with diabetes.

Strengths and limitations

This is the first study that uses patient-level data to analyse the effect of removal and reintroduction of financial incentives on the uptake of physical health checks. This allows us to make inferences on the association between the policy change and the uptake of physical health checks at patient level. Also, by controlling for patient characteristics and using a suitable physical health check as the control group, we are able to isolate changes in uptake of physical health checks that are due to the removal of incentives, from changes that are due to other factors, such as differences in patient characteristics over time and across practices, or increases in GP workload.

We used a robust methodology that allowed us to remove possible confounders and time trends that might influence the trajectory of the physical health check uptakes, and determine the causal effect.

Furthermore, our cohort design, which focused only on patients consistently registered at the same practice, prevented any loss of QOF recording data since patients who move to a new practice are exempt from QOF serious mental illness physical health check requirements for 3 months. 22

A potential caveat of our analysis is that CPRD GOLD experienced a high rate of attrition of practices from 2013 onwards, with many practices moving away from the Vision software system. 25 If practices that withdrew from CPRD GOLD in 2014 were more active in conducting the physical health checks under study, the large decline in the uptake of these checks from 2013–2014 to 2014–2015 could be explained by the dropout of these practices rather than the removal of physical health checks from QOF. However, in that case we would have continued to see the downward trend in physical health check uptake in subsequent years as attrition rates increased, which we did not observe.

As mentioned before, some caution is needed in generalising our results from CPRD GOLD because areas in the East Midlands, Yorkshire and the Humber, and the North East of England are under-represented in the data.

Comparison with existing literature

Minchin et al 20 compared the uptake trends of 12 QOF indicators for which financial incentives were removed in 2014 with the uptake trajectories of six other indicators for which incentives were maintained, including serious mental illness indicators for BMI, BP, and alcohol physical health checks. Their analysis was conducted at practice level for the period 2010–2017 and employed interrupted time series rather than a case–control difference-in-differences design. Their results were comparable to ours. They found similar trajectories for the uptake of BMI (a large reduction in the uptake immediately after its removal from QOF) and very small changes in the uptake of BP and alcohol checks. Minchin et al 20 did not study the effect of reinstating incentives for BMI measurement, which provides further reassurance that the observed results in our study are not caused by temporal confounding but reflect causal effects of QOF incentives.

Implications for practice

We found that removing incentivised physical health checks from the QOF has an immediate effect (decrease) on their uptake, which is sustained and only reversed if incentives are reinstated. This indicates that, despite physical health checks being in place for a long time, they have not become integrated into routine practice. While physical health checks function as ‘process indicators’ rather than ‘outcome indicators’, a decline in their uptake could hinder the early detection of health issues and reduce the likelihood of timely interventions, thereby compromising the quality of care. Consequently, practices should actively monitor the extent to which these documented losses in the uptake translate to real losses in quality of care, and try to ensure that the losses do not affect patients who are in need of receiving physical health checks. An implication for policymakers is that they should be cautious when considering policies of removing financial incentives from physical health checks for patients with serious mental illness. The physical health check uptake is very sensitive to these policies, possibly because this marginalised and disenfranchised patient group faces significant barriers to physical health check uptake. In that regard, the inclusion of all physical health checks back into the QOF in 2021–2022 (see Table 1 ) following a large decline in the uptake during the first pandemic year 2020–2021, 26 appears to be a step in the right direction.

• Acknowledgments

This project was undertaken on the Data Safe Haven, which is an ISO 27001 certified environment for handling sensitive data, and is provided by the University of York. We are grateful for support from the University of York Data Safe Haven team and the Research Computing team. We are grateful to all members of our advisory group and our patient and public involvement group for their valuable contributions and input.

This research is funded by the National Institute for Health and Care Research (NIHR) (Policy Research Programme, Assessing the quality and uptake of incentivised physical health checks for people with serious mental illness, reference: NIHR201421). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

Ethical approval

Ethical approval was not required for this study.

This study is based in part on data from the Clinical Practice Research Datalink (CPRD) obtained under licence from the UK Medicines and Healthcare products Regulatory Agency. The data are provided by patients and collected by the NHS as part of their care and support. The interpretation and conclusions contained in this study are those of the authors alone. The study protocol (20_000160) was approved by the CPRD’s Independent Scientific Advisory Committee.

Freely submitted; externally peer reviewed.

Competing interests

Luis Fernandes has relocated to Janssen Pharmaceutica NV, Beerse, Belgium. This disclaimer is to clarify that at the time of study development, the author was affiliated with the Centre for Health Economics, University of York. However, the author’s current affiliation at Janssen Pharmaceutica NV does not introduce any conflicts of interest regarding the research findings, data interpretation, or conclusions presented in this article. The author remains committed to upholding scientific integrity and ensuring the objectivity and impartiality of the research. All remaining authors have declared no competing interests.

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• Received October 16, 2023.
• Revision requested November 28, 2023.
• Accepted January 29, 2024.
• © The Authors

This article is Open Access: CC BY 4.0 licence ( http://creativecommons.org/licences/by/4.0/ ).

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• v.23(Suppl 4); 2019 Dec

Understanding Research Study Designs

Priya ranganathan.

Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Mumbai, Maharashtra, India

In this article, we will look at the important features of various types of research study designs used commonly in biomedical research.

How to cite this article

Ranganathan P. Understanding Research Study Designs. Indian J Crit Care Med 2019;23(Suppl 4):S305–S307.

We use a variety of research study designs in biomedical research. In this article, the main features of each of these designs are summarized.

TERMS USED IN RESEARCH DESIGNS

Exposure vs outcome.

Exposure refers to any factor that may be associated with the outcome of interest. It is also called the predictor variable or independent variable or risk factor. Outcome refers to the variable that is studied to assess the impact of the exposure on the population. It is also known as the predicted variable or the dependent variable. For example, in a study looking at nerve damage after organophosphate (OPC) poisoning, the exposure would be OPC and the outcome would be nerve damage.

Longitudinal vs Transversal Studies

In longitudinal studies, participants are followed over time to determine the association between exposure and outcome (or outcome and exposure). On the other hand, in transversal studies, observations about exposure and outcome are made at a single point in time.

Forward vs Backward Directed Studies

In forward-directed studies, the direction of enquiry moves from exposure to outcome. In backward-directed studies, the line of enquiry starts with outcome and then determines exposure.

Prospective vs Retrospective Studies

In prospective studies, the outcome has not occurred at the time of initiation of the study. The researcher determines exposure and follows participants into the future to assess outcomes. In retrospective studies, the outcome of interest has already occurred when the study commences.

CLASSIFICATION OF STUDY DESIGNS

Broadly, study designs can be classified as descriptive or analytical (inferential) studies.

Descriptive Studies

Descriptive studies describe the characteristics of interest in the study population (also referred to as sample, to differentiate it from the entire population in the universe). These studies do not have a comparison group. The simplest type of descriptive study is the case report. In a case report, the researcher describes his/her experience with symptoms, signs, diagnosis, or treatment of a patient. Sometimes, a group of patients having a similar experience may be grouped to form a case series.

Case reports and case series form the lowest level of evidence in biomedical research and, as such, are considered hypothesis-generating studies. However, they are easy to write and may be a good starting point for the budding researcher. The recognition of some important associations in the field of medicine—such as that of thalidomide with phocomelia and Kaposi's sarcoma with HIV infection—resulted from case reports and case series. The reader can look up several published case reports and case series related to complications after OPC poisoning. 1 , 2

Analytical (Inferential) Studies

Analytical or inferential studies try to prove a hypothesis and establish an association between an exposure and an outcome. These studies usually have a comparator group. Analytical studies are further classified as observational or interventional studies.

In observational studies, there is no intervention by the researcher. The researcher merely observes outcomes in different groups of participants who, for natural reasons, have or have not been exposed to a particular risk factor. Examples of observational studies include cross-sectional, case–control, and cohort studies.

Cross-sectional Studies

These are transversal studies where data are collected from the study population at a single point in time. Exposure and outcome are determined simultaneously. Cross-sectional studies are easy to conduct, involve no follow-up, and need limited resources. They offer useful information on prevalence of health conditions and possible associations between risk factors and outcomes. However, there are two major limitations of cross-sectional studies. First, it may not be possible to establish a clear cause–benefit relationship. For example, in a study of association between colon cancer and dietary fiber intake, it may be difficult to establish whether the low fiber intake preceded the symptoms of colon cancer or whether the symptoms of colon cancer resulted in a change in dietary fiber intake. Another important limitation of cross-sectional studies is survival bias. For example, in a study looking at alcohol intake vs mortality due to chronic liver disease, among the participants with the highest alcohol intake, several may have died of liver disease; this will not be picked up by the study and will give biased results. An example of a cross-sectional study is a survey on nurses’ knowledge and practices of initial management of acute poisoning. 3

Case–control Studies

Case–control studies are backward-directed studies. Here, the direction of enquiry begins with the outcome and then proceeds to exposure. Case–control studies are always retrospective, i.e., the outcome of interest has occurred when the study begins. The researcher identifies participants who have developed the outcome of interest (cases) and chooses matching participants who do not have the outcome (controls). Matching is done based on factors that are likely to influence the exposure or outcome (e.g., age, gender, socioeconomic status). The researcher then proceeds to determine exposure in cases and controls. If cases have a higher incidence of exposure than controls, it suggests an association between exposure and outcome. Case–control studies are relatively quick to conduct, need limited resources, and are useful when the outcome is rare. They also allow the researcher to study multiple exposures for a particular outcome. However, they have several limitations. First, matching of cases with controls may not be easy since many unknown confounders may affect exposure and outcome. Second, there may be biased in the way the history of exposure is determined in cases vs controls; one way to overcome this is to have a blinded assessor determining the exposure using a standard technique (e.g., a standardized questionnaire). However, despite this, it has been shown that cases are far more likely than controls to recall history of exposure—the “recall bias.” For example, mothers of babies born with congenital anomalies may provide a more detailed history of drugs ingested during their pregnancy than those with normal babies. Also, since case-control studies do not begin with a population at risk, it is not possible to determine the true risk of outcome. Instead, one can only calculate the odds of association between exposure and outcome.

Kendrick and colleagues designed a case–control study to look at the association between domestic poison prevention practices and medically attended poisoning in children. They identified children presenting with unintentional poisoning at home (cases with the outcome), matched them with community participants (controls without the outcome), and then elicited data from parents and caregivers on home safety practices (exposure). 4

Cohort Studies

Cohort studies resemble clinical trials except that the exposure is naturally determined instead of being decided by the investigator. Here, the direction of enquiry begins with the exposure and then proceeds to outcome. The researcher begins with a group of individuals who are free of outcome at baseline; of these, some have the exposure (study cohort) while others do not (control group). The groups are followed up over a period of time to determine occurrence of outcome. Cohort studies may be prospective (involving a period of follow-up after the start of the study) or retrospective (e.g., using medical records or registry data). Cohort studies are considered the strongest among the observational study designs. They provide proof of temporal relationship (exposure occurred before outcome), allow determination of risk, and permit multiple outcomes to be studied for a single exposure. However, they are expensive to conduct and time-consuming, there may be several losses to follow-up, and they are not suitable for studying rare outcomes. Also, there may be unknown confounders other than the exposure affecting the occurrence of the outcome.

Jayasinghe conducted a cohort study to look at the effect of acute organophosphorus poisoning on nerve function. They recruited 70 patients with OPC poisoning (exposed group) and 70 matched controls without history of pesticide exposure (unexposed controls). Participants were followed up or 6 weeks for neurophysiological assessments to determine nerve damage (outcome). Hung carried out a retrospective cohort study using a nationwide research database to look at the long-term effects of OPC poisoning on cardiovascular disease. From the database, he identified an OPC-exposed cohort and an unexposed control cohort (matched for gender and age) from several years back and then examined later records to look at the development of cardiovascular diseases in both groups. 5

Interventional Studies

In interventional studies (also known as experimental studies or clinical trials), the researcher deliberately allots participants to receive one of several interventions; of these, some may be experimental while others may be controls (either standard of care or placebo). Allotment of participants to a particular treatment arm is carried out through the process of randomization, which ensures that every participant has a similar chance of being in any of the arms, eliminating bias in selection. There are several other aspects crucial to the validity of the results of a clinical trial such as allocation concealment, blinding, choice of control, and statistical analysis plan. These will be discussed in a separate article.

The randomized controlled clinical trial is considered the gold standard for evaluating the efficacy of a treatment. Randomization leads to equal distribution of known and unknown confounders between treatment arms; therefore, we can be reasonably certain that any difference in outcome is a treatment effect and not due to other factors. The temporal sequence of cause and effect is established. It is possible to determine risk of the outcome in each treatment arm accurately. However, randomized controlled trials have their limitations and may not be possible in every situation. For example, it is unethical to randomize participants to an intervention that is likely to cause harm—e.g., smoking. In such cases, well-designed observational studies are the only option. Also, these trials are expensive to conduct and resource-intensive.

In a randomized controlled trial, Li et al. randomly allocated patients of paraquat poisoning to receive either conventional therapy (control group) or continuous veno-venous hemofiltration (intervention). Patients were followed up to look for mortality or other adverse events (outcome). 6

Researchers need to understand the features of different study designs, with their advantages and limitations so that the most appropriate design can be chosen for a particular research question. The Centre for Evidence Based Medicine offers an useful tool to determine the type of research design used in a particular study. 7

Source of support: Nil

Conflict of interest: None