Abstract
IntroductionMost literature on optimal group-sequential designs focuses on minimising the expected sample size. We highlight other factors for consideration. MethodsWe discuss several quantities less-often considered in adaptive design: the median and standard deviation of the random required sample size, and the probability of committing an interim error. We consider how the optimal timing of interim analyses changes when these quantities are accounted for. ResultsIncorporating the standard deviation of the required sample size into an optimality framework, we demonstrate how and when this quantity means using a group-sequential approach is not optimal. The optimal timing of an interim analysis is shown to be highly dependent on the pre-specified preference for minimising the expected sample size relative to its standard deviation. ConclusionsExamining multiple factors, which measure the advantages and disadvantages of group-sequential designs, helps determine the best design for a specific trial.
Highlights
Most literature on optimal group-sequential designs focuses on minimising the expected sample size
We focus on two-stage designs (J = 2); some results for J = 3 are given in the Supplementary Materials
In a GS design, several issues can arise, for example, it may not be clear what will happen to trial staff if a study terminates early
Summary
Most literature on optimal group-sequential designs focuses on minimising the expected sample size. Methods: We discuss several quantities less-often considered in adaptive design: the median and standard devi ation of the random required sample size, and the probability of committing an interim error. Results: Incorporating the standard deviation of the required sample size into an optimality framework, we demonstrate how and when this quantity means using a group-sequential approach is not optimal. The optimal timing of an interim analysis is shown to be highly dependent on the pre-specified preference for minimising the expected sample size relative to its standard deviation. The majority of these minimise a weighted combination of expected sample sizes (ESSs) at particular treatment effects
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