A Note on Adaptively Changing the Hierarchy of Hypotheses in Clinical Trials with Flexible Design

Abstract

Flexible designs with adaptive interim analyses allow a data-driven modification of basic design features while controlling the prespecified type I error rate. For example, an adaptation of the initially planned sample size based on the observed treatment effect and/or variability is possible, as well as an alteration of the number and timing of planned interim analyses, selection of doses or early stopping of treatment groups in multi-armed trials, or a change of the test statistic prespecified for the analysis. Recently, a multiple test procedure has been proposed which enables a modification of the initially specified hypotheses structure for confirmatory analysis. In this case, the initially fixed order of hypotheses can be altered on the basis of the results of an interim analysis. In a Monte Carlo study, the power performance of the proposed procedure of adaptively changing the hierarchy of hypotheses is compared with alternative testing strategies that do not change the initially specified structure of the hypotheses. Our investigations indicate that imposing a hierarchy of hypotheses and relying on the option for an adaptation may be an inefficient strategy if ordering is not done due to clinical importance but according to expectations about the statistical significance of the results. Alternatively, multiple test procedures that do not require an a priori order may be applied.