  Phase III randomized clinical trials play a monumentally critical role in the
evaluation of new medical products. Because of the intrinsic nature of
uncertainty embedded in our capability in assessing the efficacy of a medical
product, interpretation of trial results relies on statistical principles to
control the error of false positives below desirable level. The
well-established statistical hypothesis testing procedure suffers from two
major limitations, namely, the lack of flexibility in the thresholds to claim
success and the lack of capability of controlling the total number of false
positives that could be yielded by the large volume of trials. We propose two
general theoretical frameworks based on the conventional frequentist paradigm
and Bayesian perspectives, which offer realistic, flexible and effective
solutions to these limitations. Our methods are based on the distribution of
the effect sizes of the population of trials of interest. The estimation of
this distribution is practically feasible as clinicaltrials.gov provides a
centralized data repository with unbiased coverage of clinical trials. We
provide a detailed development of the two frameworks with numerical results
obtained for industry sponsored Phase III randomized clinical trials.
