Design aspects for clinical trials with repeated measures in the presence of informative censoring

Abstract

Likelihood-based procedures derived under the linear random effects model and distribution-free tests obtained by combining marginal U statistics as well as by ranking the individual effects are reviewed for clinical trials with repeated measures. Simulation studies are illustrated to evaluate the effects of informative censoring and model misspecifications on statistical power and sample size. The results indicate that in some situations with information censoring the combined marginal U statistic could suffer severe loss of power. Furthermore, in the presence of informative censoring with some model misspecifications all other procedures could also suffer some power losses, although not as severe. Therefore, in planning clinical trials in which informative censoring is likely to occur, it is important to conduct some simulation studies based on realistic parameter estimates such that a proper method of analysis could be selected and appropriate sample size adjustments could be made.