Adjusting sample size for anticipated dropouts in clinical trials

Statistical models for calculating sample sizes for controlled clinical trials often fail to take into account the negative impact that dropouts have on the power of intent-to-treat analyses. Empirically defined dropout correction coefficients are proposed to adjust sample sizes for endpoint analysis of variance (ANOVA) and analysis of covariance (ANCOVA) that have been initially calculated assuming complete data. The implications of type of analysis (change-score ANOVA or ANCOVA), correlational structure of the repeated measurements (compound symmetry or autoregressive), and percentage of dropouts (20% or 30%) are considered, together with other less influential design and data parameters. We recommend the use of ANCOVA to correct for baseline differences and for time-in-study if there is a nonspecific change across time. Given a realistic autoregressive (order 1) correlational structure for the repeated measurements and a proposed endpoint ANCOVA, the empirical results support the common practice of increasing calculated sample size by the anticipated number of dropouts. The previous rationale has been to retain a requisite number of "completers" on which to base statistical inferences. We believe the present results provide the first documentation of the relevance of that strategy for intent-to-treat analyses in which the incomplete data for dropouts must be included. Based on comparative power analyses, the strategy also seems appropriate for maintaining the power of mixed-model regression analyses, simple regression on a normalized time scale, and analyses of trends fitted to imputed scores for dropouts.