Abstract
Overview Cancer research is rapidly changing, with fast‐growing numbers of possible cancer targets and drugs to investigate and no end in sight. Advances in genomics, proteomics, epigenetics, and immune oncology provide remarkably detailed profiles of each patient's tumor and its microenvironment and, as a result, allow science to consider each patient as unique, with the goal of delivering individualized precision medicine. The low success rates of late‐phase clinical oncology trials, high costs, and long duration of bringing new drugs to market, however, have necessitated changes in the drug‐development process. Statistical innovations can help in the design and conduct of clinical trials to facilitate the discovery and validation of biomarkers and streamline the clinical trial process. The application of Bayesian statistics provides a sound theoretical foundation that can encourage the development of adaptive designs to improve trial flexibility and efficiency, while maintaining desirable statistical‐operating characteristics.
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