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Phase III randomized clinical trials play a monumentally critical role in theevaluation of new medical products. Because of the intrinsic nature ofuncertainty embedded in our capability in assessing the efficacy of a medicalproduct, interpretation of trial results relies on statistical principles tocontrol the error of false positives below desirable level. Thewell-established statistical hypothesis testing procedure suffers from twomajor limitations, namely, the lack of flexibility in the thresholds to claimsuccess and the lack of capability of controlling the total number of falsepositives that could be yielded by the large volume of trials. We propose twogeneral theoretical frameworks based on the conventional frequentist paradigmand Bayesian perspectives, which offer realistic, flexible and effectivesolutions to these limitations. Our methods are based on the distribution ofthe effect sizes of the population of trials of interest. The estimation ofthis distribution is practically feasible as clinicaltrials.gov provides acentralized data repository with unbiased coverage of clinical trials. Weprovide a detailed development of the two frameworks with numerical resultsobtained for industry sponsored Phase III randomized clinical trials.

Towards More Flexible False Positive Control in Phase III Randomized Clinical Trials