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

TL;DR

This paper introduces flexible statistical frameworks within frequentist and Bayesian paradigms to improve false positive control in Phase III clinical trials, leveraging effect size distributions from clinical trial data.

Contribution

It develops two novel theoretical frameworks that enhance false positive control and flexibility in significance thresholds for large-scale clinical trial analysis.

Findings

Frameworks effectively control false positives in simulated trials.

Application to real Phase III data demonstrates practical utility.

Methods offer adaptable solutions for trial success criteria.

Abstract

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.