Adapting sample size calculations to repeated measurements in clinical trials

Many of the repeated-measures sample size calculation methods presented in the literature are not suitable when: the different treatments are assumed to be equal on average at baseline time due to randomization, and the experimenters are interested in a pre-specified difference to be detected after a specific time period. The method presented here has been developed for those cases where a multivariate normal distribution can reasonably be assumed. It is likelihood-based and has been designed to be flexible enough to handle repeated-measures models, including a nonlinear change in time, and an arbitrary correlation structure.