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Abstract:
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Precision medicine endeavors to conform therapeutic interventions to the individuals being treated and needs to account for the heterogeneity of treatment benefit among patients and patient subpopulations. In oncology, basket trials have emerged as a popular design to better address the goals of precision medicine that endeavors to test the effectiveness of a therapeutic strategy among patients defined by the presence of a particular biomarker target rather than cancer, where the evaluation of treatment effectiveness are conducted with respect to the "baskets" which represent a partition of the targeted patient population. However, many basket trials may be incorporating inefficient approaches to interim monitoring and sharing information across baskets where they may be potentially exchangeable. We present novel methodology for a sequential basket trial design using Bayesian interim analyses with predictive probability monitoring and the incorporation of a novel hierarchical modeling strategy for sharing information among a collection of discrete, potentially non-exchangeable subtypes that we contrast with the popular, but inefficient, Simon two-stage design.
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