Show Summary Details
Page of

Observational Studies 

Observational Studies
Study Design and Statistics

Margaret A. Winker

and Stephen J. Lurie

Page of

PRINTED FROM AMA MANUAL OF STYLE ONLINE ( © American Medical Association, 2009. All Rights Reserved. Under the terms of the license agreement, an individual user may print out a PDF of a single chapter of a title in AMA Manual of Style Online for personal use (for details see Privacy Policy and Legal Notice).

Subscriber: null; date: 20 June 2018

Observational Studies

In an observational study, the researcher identifies a condition or outcome of interest and then measures factors that may be related to that outcome. Although observational studies cannot lead to strong causal inferences, they may nonetheless suggest certain causal hypotheses. To infer causation in observational studies, investigators attempt to establish a sequence of events—if event A generally precedes event B in time, then it is possible that A may be responsible for causing B. Such studies may be either retrospective (the investigator tries to reconstruct what happened in the past) or prospective (the investigator identifies a group of individuals and then observes them for a specified period of time). Prospective studies generally yield more reliable conclusions than do retrospective studies.

Cross-sectional studies observe individuals at a single point in time. Such studies may be helpful for suggesting relationships among variables but cannot address whether one condition may precede or follow another. Thus, cross-sectional studies cannot establish causation, but they may nonetheless be helpful for suggesting hypotheses to guide more rigorous studies.

Because individuals in observational studies are not randomly assigned to conditions, there are often large baseline differences between groups in such studies. For instance, individuals with better exercise habits often differ in a number of important ways (eg, education, income, diet, smoking) from those who do not exercise regularly. Because exercise is confounded with these variables, it is difficult to know whether exercise itself is responsible for any differences in health outcomes. Researchers may use several different statistical techniques to minimize the effects of confounding, including matching, stratification, multivariate analysis, and propensity analysis.

Even with the most extensive attempts to minimize confounding, it is always possible that results of observational studies may in fact be due to other variables that the authors did not measure. Because of this unavoidable possibility of residual confounding in observational studies, the results are generally not as reliable as those of RCTs. Sometimes the results of observational studies may differ significantly from those of RCTs.12 On the other hand, because observational studies are more often based on the outcomes of a large range of people in realistic situations, they may add useful insights to disease processes as they occur beyond the limited conditions of RCTs. Furthermore, observational studies may be the only way to investigate certain problems (eg, automobile crashes, exposure to toxic chemicals) for which it would be unethical to perform RCTs.

There are currently no standardized guidelines for reporting the various types of observational studies. Although the CONSORT group is currently developing such guidelines for case-control and cohort studies, it is unclear at this time whether they will become as widely accepted as the CONSORT guidelines for RCTs. Current information can be found on the CONSORT website (

Previous | Next