Inference for Multi Marker Adaptive Enrichment Trials - Simon, Richard (Stat Med, 2017)
Identification of treatment selection biomarkers has become very important in cancer drug development. Adaptive enrichment designs have been developed for situations where a unique treatment selection biomarker is not apparent based on the mechanism of action of the drug, With such designs, the eligibility rules may be adaptively modified at interim analysis times in order to exclude patients who are unlikely to benefit from the test treatment. The adaptive enrichment approach of Simon and Simon [1] is particularly flexible, permitting development of model based multi-feature predictive classifiers as well as optimized cut-points for continuous biomarkers. A single significance test, including all randomized patients is performed at the end of the trial of the strong null hypothesis that the expected outcome on the test treatment is no better than control for any of the subset populations of patients accrued in the K stages of the clinical trial. In this paper we address two issues involving inference following a Simon and Simon [1] type of adaptive enrichment design. The first is specification of the intended use population and estimation of treatment effect for that population following rejection of the strong null hypothesis. The second issue is defining conditions in which rejection of the strong null hypothesis implies rejection of the null hypothesis for the intended use population.