Background: Many clinical trials have a crossover design. Certain considerations that are relevant to the crossover design, but play no role in standard parallel-group trials, must receive adequate attention in trial planning and data analysis for the results to be of scientific value.

Methods: The authors present the basic statistical methods required for the analysis of crossover trials, referring to standard statistical texts.

Results: In the simplest and most common scenario, a crossover trial involves two treatments which are consecutively administered in each patient recruited in the study. The main purpose served by the design is to provide a basis for separating treatment effects from period effects. This is achieved via computing the treatment effects separately in two sequence groups formed via randomization. The differences between treatment effects can be assessed by means of a standard t-test for independent samples using the intra-individual differences between the outcomes in both periods as the raw data. The existence of carryover effects must be ruled out for this method to be valid. This assumption is usually checked using a pre-test, which is also described in this article. Finally, we briefly discuss the use of nonparametric tests instead of t-tests and more complicated designs with more than two test periods and/or treatments.

Conclusion: Crossover trials in which the results are not analyzed separately by sequence group are of limited, if any, scientific value. It is also essential to guard against carryover effects. Whenever ignoring such effects proves unjustified, the treatment effect must be analyzed solely via an analysis of the data obtained during the first trial period. Even the use of this restricted dataset yields results whose validity is not beyond question.