Sample size software is readily available for many univariate statistical procedures that involve one dependent variable per subject However, power analysis is less available for designs with repeated measures, particularly for repeated binary outcome variables. Repeated measurement studies usually involve missing data and serial correlations within each subject. As a consequence, generalized estimating equation (GEE) models, which do not require complete data for all subjects and which do not depend on the restrictive “symmetry assumption,” are being increasingly recommended for the evaluation of treatment effects in controlled clinical trials. GEE has been widely used to examine whether the rates of changes are significantly different between groups due to its robustness to misspecification of the true correlation structure and randomly missing data. We illustrate how the sample size can be estimated in detail through examples and also present the effect of dropouts in the sample size estimate for the repeated binary outcomes. In the presence of dropouts, it is suggested to estimate the sample size using the closed form formula provided in this article instead of adjusting the sample size estimate by dividing the sample size estimate obtained under no missing data by the proportions of completers.
- Compound symmetry
- Independent missing
- Monotone missing
ASJC Scopus subject areas
- Pharmacology, Toxicology and Pharmaceutics (miscellaneous)
- Public Health, Environmental and Occupational Health
- Pharmacology (medical)