Skip to main content
Sign In

Study Design

There are several different types of study designs which can be classified into two main categories: observational and experimental. Each study design has unique strengths and weaknesses which must be considered when determining the most appropriate design to test a study hypothesis.

Observational Studies


Studies of risk factors on health or other outcomes based on population or group (aggregate) data and not individual level data.

  • Pro: Low cost, convenient, and hypothesis generating.
  • Con: Heterogeneity of exposure, and lack of covariates at the individual level.
Case Series

Identify a group of individuals with an outcome of particular interest and describe the characteristics of the group.

  • Outcome measures: Estimate prevalence, only have prevalent cases.
  • Pro: Quick and easy, cheap to conduct, descriptive, and hypothesis generating.
  • Con: Can’t assess causality, incidence-prevalence bias, and bias by time.

Identify subjects before they have the outcome of interest.

  • Prospective: A sample is selected based on exposure status (exposed and unexposed/control group) and the study participants are followed "longitudinally" i.e., over a period of time, for disease development or outcome of interest.
  • Retrospective: The investigators use data that has already been collected to identify a cohort of exposed/unexposed individuals at a point in time before they developed the outcome of interest (i.e., medical records) and then use the already collected follow-up time calculate risk of disease development.
  • Pro: Temporality is established, multiple outcomes, can use when randomization is unethical, direct estimate of effect, rare exposures, and matching to control for confounding.
  • Con: Time consuming and expensive (prospective), not randomly assigned exposure, confounding, selection bias, doesn’t work well for rare diseases, and loss to follow-up.

Study that compares patients who have the disease of interest (cases) with patients who do not have the disease of interest (controls - who are from the same source population as the cases) and then looks back retrospectively to compare how frequently the exposure to a risk factor is present in each group to determine the relationship between the risk factor and disease.

  • Outcome measures: Odds Ratio is the correct measure of association.
  • Pro: Efficient, good for rare diseases, and look at multiple exposures simultaneously.
  • Con: Recall bias, differential misclassification of exposure status, hard to establish temporality, difficulty of selecting control group, and doesn’t work well for rare exposures.

Experimental Studies

Randomized Controlled Trial

Study participants are randomly allocated to receive one or more clinical interventions/treatments or placebo. After investigator controls exposure then the study participants are followed-up for outcomes of interest. This type of study is the gold standard clinical trial and is often used to test efficacy or effectiveness of various types of medical interventions.

  • Pro: Gold standard, internally valid, temporality established, minimize confounding as participants are randomized to treatment groups.
  • Con: Can’t use for unethical treatments, expensive, time consuming, powered for efficacy and not adverse events, selection bias, and may lack external validity.
  • Types:
    • Clinical trials (Drugs, FDA, etc. - subjects usually have disease or illness).
    • Field trials (Subjects not diseased, usually longer than clinical trials, involve visiting subjects in field i.e., home, work, etc.).
Crossover study/trial

A longitudinal study in which study participants receive a sequence of different treatments (or exposures) of interest during different time periods, i.e. the patients cross over from one treatment to another during the course of the trial with a predetermined "wash-out" periods between treatments. This type of study design can be experimental or observational in nature.

  • Pro: Cases serve as own control reducing confounding variables between subjects.
  • Con: The "wash-out" period needs to be long enough to see independent effects of the two treatments.

Manipulate intervention but do not randomize subjects. Known as the "Natural experiment" and exposure is often dictated by policy or legislation (i.e., seat belt law).

  • Pro: Less expensive than trial, population-based, cost/benefit analysis, fewer ethical issues than trial.
  • Con: Difficult to control for confounding.

Errors arising in various study designs

Random error
Non-random error (systematic)
  • Information bias (measurement error)
    • Subject/respondent bias
    • Recall bias
    • Reporting bias
  • Selection bias
  • Confounding: Distortion of the exposure-outcome association due to their mutual association with another factor. In order for a variable to be considered a confounder, it has to be associated with the exposure of interest and cause (or precede) the disease/outcome of interest.
  • Mediation: A mediator is present when the relationship between your exposure of interest (x) and your outcome (y) is mediated by a third variable Z. In other words, your mediation variable (z) is on the causal pathway between x and y.

Center for Innovative Design & Analysis (CIDA)

Formerly known as the Colorado Biostatistics Consortium (CBC)
13001 17th Place | Mail Stop B119 | Room 100, Building 406 | Aurora, CO 80045
303.724.2325 |

Biostatistics Consulting | Grant Collaboration | Department of Biostatistics and Informatics | BERD | BBSR | ColoradoSPH

© The Regents of the University of Colorado, a body corporate. All rights reserved.

Accredited by the Higher Learning Commission. All trademarks are registered property of the University. Used by permission only.