ABSTRACT
Despite a great effort, we often face a high degree of uncertainty about
parameters when designing a trial or justifying the sample size at the design
stage. This could involve the initial estimates of within-or between-patient
variation, a control group event rate for a binary outcome, the treatment
effect desired to be detected, the recruiting pattern, or patient compliance,
all of which affect the ability of the trial to address its primary objective
(Shih, 2001). This uncertainty can also include the correlation between the
measures (if a repeated measure model is used) or among different variables
(multiple endpoints, covariates). If a small uncertainty of prior information
exists, a classical design can be applied. However, when the uncertainty is
greater, a classical design with a fixed sample size is inappropriate. Instead,
it is desirable to have a trial design that allows for reestimation of sample
size in the middle of the trial based on unblinded data. Several different
algorithms have been proposed for sample-size reestimation, including the
conditional power approach and Cui-Hung-Wang’s approach based on the
ratio of observed effect size versus expected effect size.