Patient-centered clinical trials. Issue 2 (February 2018) - Chaudhuri, Shomesh E.
Highlights: Traditional interpretations of clinical trials use fixed p -value thresholds. Patients may prefer higher risk of false positives in some cases. We propose using Bayesian decision analysis to incorporate such preferences. We apply this framework to actual clinical-trial data for weight-loss devices. Patient-centered p -values can differ substantially from standard values. Abstract : We apply Bayesian decision analysis (BDA) to incorporate patient preferences in the regulatory approval process for new therapies. By assigning weights to type I and type II errors based on patient preferences, the significance level ( α ) and power (1 − β ) of a randomized clinical trial (RCT) for a new therapy can be optimized to maximize the value to current and future patients and, consequently, to public health. We find that for weight-loss devices, potentially effective low-risk treatments have optimal α s larger than the traditional one-sided significance level of 5%, whereas potentially less effective and riskier treatments have optimal α s below 5%. Moreover, the optimal RCT design, including trial size, varies with the risk aversion and time-to-access preferences and the medical need of the target population.