Types of Study Design

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Study designs are frameworks used in medical research to gather data and explore a specific research question.

Choosing an appropriate study design is one of many essential considerations before conducting research to minimise bias and yield valid results.

This guide provides a summary of study designs commonly used in medical research, their characteristics, advantages and disadvantages.

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Case-report and case-series

A case report is a detailed description of a patient’s medical history, diagnosis, treatment, and outcome. A case report typically documents unusual or rare cases or reportsΒ new or unexpected clinical findings.

A case series is a similar study that involves a group of patients sharing a similar disease or condition. A case series involves a comprehensive review of medical records for each patient to identify common features or disease patterns. Case series help better understand a disease’s presentation, diagnosis, and treatment.

While a case report focuses on a single patient, a case series involves a group of patients to provide a broader perspective on a specific disease. Both case reports and case series are important tools for understanding rare or unusual diseases.


Advantages of case series and case reports include:

  • Able to describe rare or poorly understood conditions or diseases
  • Helpful in generating hypotheses and identifying patterns or trends in patient populations
  • Can be conducted relatively quickly and at a lower cost compared to other research designs


Disadvantages of case series and case reports include:

  • Prone to selection bias, meaning that the patients included in the series may not be representative of the general population
  • Lack a control group, which makes it difficult to concludeΒ the effectiveness of different treatments or interventions
  • They are descriptive and cannot establish causality or control for confounding factors

Cross-sectional study

A cross-sectional study aims to measure the prevalence or frequency of a disease in a population at a specific point in time. In other words, it provides a “snapshot” of the population at a single moment in time.

Cross-sectional studies are unique from other study designs in that they collect data on the exposure and the outcome of interest from a sample of individuals in the population. This type of data is used to investigate the distribution of health-related conditions and behaviours in different populations, which is especially useful for guiding the development of public health interventions.

Example of a cross-sectional study

A cross-sectional study might investigate the prevalence of hypertension (the outcome) in a sample of adults in a particular region. The researchers would measure blood pressure levels in each participant and gather information on other factors that could influence blood pressure, such as age, sex, weight, and lifestyle habits (exposure).


Advantages of cross-sectional studies include:

  • Relatively quick and inexpensive to conduct compared to other study designs, such as cohort or case-control studies
  • They can provide a snapshot of the prevalence and distribution of a particular health condition in a population
  • They can help to identify patterns and associations between exposure and outcome variables, which can be used to generate hypotheses for further research


Disadvantages of cross-sectional studies include:

  • They cannot establish causality, as they do not follow participants over time and cannot determine the temporal sequence between exposure and outcome
  • Prone to selection bias, as the sample may not represent the entire population being studied
  • They cannot account for confounding variables, which may affect the relationship between the exposure and outcome of interest

Case-control study

A case-control study compares people who have developed a disease of interest (cases) with people who have not developed the disease (controls) to identify potential risk factors associated with the disease.

Once cases and controls have been identified, researchers then collect information about related risk factors, such as age, sex, lifestyle factors, or environmental exposures, from individuals. By comparing the prevalence of risk factors between the cases and the controls, researchers can determine the association between the risk factors and the disease.

Example of a case-control study

A case-control study design might involve comparing a group of individuals with lung cancer (cases) to a group of individuals without lung cancer (controls) to assess the association between smoking (risk factor) and the development of lung cancer.


Advantages of case-control studies include:

  • Useful for studying rare diseases, as they allow researchers to selectively recruit cases with the disease of interest
  • Useful for investigating potential risk factors for a disease, as the researchers can collect data on many different factors from both cases and controls
  • Can be helpful in situations where it is not ethical or practical to manipulate exposure levels or randomise study participants


Disadvantages of case-control studies include:

  • Prone to selection bias, as the controls may not be representative of the general population or may have different underlying risk factors than the cases
  • Cannot establish causality, as they can only identify associations between factors and disease
  • May be limited by the availability of suitable controls, as finding appropriate controls who have similar characteristics to the cases can be challenging

Cohort study

A cohort study follows a group of individuals (a cohort) over time to investigate the relationship between an exposure or risk factor and a particular outcome or health condition. Cohort studies can be further classified into prospective or retrospective cohort studies.

Prospective cohort study

A prospective cohort study is a study in which the researchers select a group of individuals who do not have a particular disease or outcome of interest at the start of the study.

They then follow this cohort over time to track the number of patients who develop the outcome. Before the start of the study, information on exposure(s) of interest may also be collected.

Example of a prospective cohort study

A prospective cohort study might follow a group of individuals who have never smoked and measure their exposure to tobacco smoke over time to investigate the relationship between smoking and lung cancer.

Retrospective cohort study

In contrast, a retrospective cohort study is a study in which the researchers select a group of individuals who have already been exposed to something (e.g. smoking) and look back in time (for example, through patient charts) to see if they developed the outcome (e.g. lung cancer).

The key difference in retrospective cohort studies is that data on exposure and outcome are collected after the outcome has occurred.

Example of a retrospective cohort study

A retrospective cohort study might look at the medical records of smokers and see if they developed a particular adverse event such as lung cancer.


Advantages of cohort studies include:

  • Generally considered to be the most appropriate study design for investigating the temporal relationship between exposure and outcome
  • Can provide estimates of incidence and relative risk, which are useful for quantifying the strength of the association between exposure and outcome
  • Can be used to investigate multiple outcomes or endpoints associated with a particular exposure, which can help to identify unexpected effects or outcomes


Disadvantages of cohort studies include:

  • Can be expensive and time-consuming to conduct, particularly for long-term follow-up
  • May suffer from selection bias, as the sample may not be representative of the entire population being studied
  • May suffer from attrition bias, as participants may drop out or be lost to follow-up over time


A meta-analysis is a type of study that involves extracting outcome data from all relevant studies in the literature and combining the results of multiple studies to produce an overall estimate of the effect size of an intervention or exposure.

Meta-analysis is often conducted alongside a systematic review and can be considered a study of studies. By doing this, researchers provide a more comprehensive and reliable estimate of the overall effect size and their confidence interval (a measure of precision).

Meta-analyses can be conducted for a wide range of research questions, including evaluating the effectiveness of medical interventions, identifying risk factors for disease, or assessing the accuracy of diagnostic tests. They are particularly useful when the results of individual studies are inconsistent or when the sample sizes of individual studies are small, as a meta-analysis can provide a more precise estimate of the true effect size.

When conducting a meta-analysis, researchers must carefully assess the risk of bias in each study to enhance the validity of the meta-analysis. Many aspects of research studies are prone to bias, such as the methodology and the reporting of results. Where studies exhibit a high risk of bias, authors may opt to exclude the study from the analysis or perform a subgroup or sensitivity analysis.


Advantages of a meta-analysis include:

  • Combine the results of multiple studies, resulting in a larger sample size and increased statistical power, to provide a more comprehensive and precise estimate of the effect size of an intervention or outcome
  • Can help to identify sources of heterogeneity or variability in the results of individual studies by exploring the influence of different study characteristics or subgroups
  • Can help to resolve conflicting results or controversies in the literature by providing a more robust estimate of the effect size


Disadvantages of a meta-analysis include:

  • Susceptible to publication bias, where studies with statistically significant or positive results are more likely to be published than studies with nonsignificant or negative results. This bias can lead to an overestimation of the treatment effect in a meta-analysis
  • May not be appropriate if the studies included are too heterogeneous, as this can make it difficult to draw meaningful conclusions from the pooled results
  • Depend on the quality and completeness of the data available from the individual studies and may be limited by the lack of data on certain outcomes or subgroups

Ecological study

An ecological study assesses the relationship between outcome and exposure at a population level or among groups of people rather than studying individuals directly.

The main goal of an ecological study is to observe and analyse patterns or trends at the population level and to identify potential associations or correlations between environmental factors or exposures and health outcomes.

Ecological studies focus on collecting data on population health outcomes, such as disease or mortality rates, and environmental factors or exposures, such as air pollution, temperature, or socioeconomic status.

Example of an ecological study

An ecological study might be used when comparing smoking rates and lung cancer incidence across different countries.


Advantages of an ecological study include:

  • Provide insights into how social, economic, and environmental factors may impact health outcomes in real-world settings, which can inform public health policies and interventions
  • Cost-effective and efficient, often using existing data or readily available data, such as data from national or regional databases


Disadvantages of an ecological study include:

  • Ecological fallacy occurs when conclusions about individual-level associations are drawn from population-level differences
  • Ecological studies rely on population-level (i.e. aggregate) rather than individual-level data; they cannot establish causal relationships between exposures and outcomes, as the studies do not account for differences or confounders at the individual level

Randomised controlled trial

A randomised controlled trial (RCT) is an important study design commonly used in medical research to determine the effectiveness of a treatment or intervention. It is considered the gold standard in research design because it allows researchers to draw cause-and-effect conclusions about the effects of an intervention.

In an RCT, participants are randomly assigned to two or more groups. One group receives the intervention being tested, such as a new drug or a specific medical procedure. In contrast, the other group is a control group and receives either no intervention or a placebo.

Randomisation ensures that each participant has an equal chance of being assigned to either group, thereby minimising selection bias. To reduce bias, an RCT often uses a technique called blinding, in which study participants, researchers, or analysts are kept unaware of participant assignment during the study. The participants are then followed over time, and outcome measures are collected and compared to determine if there is any statistical difference between the intervention and control groups.

Example of a randomised controlled trial

An RCT might be employed to evaluate the effectiveness of a new smoking cessation program in helping individuals quit smoking compared to the existing standard of care.


Advantages of an RCT include:

  • Considered the most reliable study design for establishing causal relationships between interventions and outcomes and determining the effectiveness of interventions
  • Randomisation of participants to intervention and control groups ensures that the groups are similar at the outset, reducing the risk of selection bias and enhancing internal validity
  • Using a control group allows researchers to compare with the group that received the intervention while controlling for confounding factors


Disadvantages of an RCT include:

  • Can raise ethical concerns; for example, it may be considered unethical to withhold an intervention from a control group, especially if the intervention is known to be effective
  • Can be expensive and time-consuming to conduct, requiring resources for participant recruitment, randomisation, data collection, and analysis
  • Often have strict inclusion and exclusion criteria, which may limit the generalisability of the findings to broader populations
  • May not always be feasible or practical for certain research questions, especially in rare diseases or when studying long-term outcomes


Dr Chris Jefferies


  • Yuliya L, Qazi MA (eds.). Toronto Notes 2022. Toronto: Toronto Notes for Medical Students Inc; 2022.
  • Le T, Bhushan V, Qui C, Chalise A, Kaparaliotis P, Coleman C, Kallianos K. First Aid for the USMLE Step 1 2023. New York: McGraw-Hill Education; 2023.
  • Rothman KJ, Greenland S, Lash T. Modern Epidemiology. 3rd ed. Philadelphia: Lippincott Williams & Wilkins; 2008.


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