Multiplicity Control in Clinical Trials with Adaptive Selection Followed by Group-Sequential Testing

We propose a strategy for managing the issue of multiplicity in clinical trials with adaptive selection followed by group-sequential testing. The approach employs a two-stage design and addresses trials with multiple hypotheses. The first stage adaptively selects a subset of hypotheses for further testing, while the second stage monitors the remaining hypotheses based on group-sequential procedures. We provide a rigorous framework for controlling the overall Type-1 error rate across both stages, utilizing group-sequential p-values and the closed testing principle to ensure statistical validity in the adaptive setting. Through a simulation study based on an oncology trial example, we demonstrate the effectiveness of the proposed method in controlling Type-1 error rate while maintaining sufficient power.