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Abstract:
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In the recently oncology clinical trials, biomarker subpopulation (biomarker +/- or all comers) selections have become increasingly important for streamlining drug development in tailored therapies and fulfilling regulatory commitment. Based on the unmet medical need, biomarker subpopulation selections towards personalized medicine paradigm are also embraced by regulatory agencies with the option for accelerated approval when greater benefit is found in certain biomarker-defined subpopulation comparing to an all-comer situation. In addition, it is common to include both progression-free survival (PFS) and overall survival (OS) as key efficacy endpoints to assess the main clinical benefits of the treatment. It is desirable to optimize the study design to have a more enhanced claim in which multiple objectives can be met. However, multiple testing procedures need to be planned with caution and aligned with clinical objectives to maximize the study power. In this work, multiplicity adjustment strategies with multiple endpoints and subpopulations will be discussed. Specifically, the alpha-splitting and step-down approaches will be illustrated using a hypothetical clinical trial exa
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