Decision Making in Drug Development via Confidence Distributions

Abstract

In clinical drug development a typical phase three power calculation for a Go/No-Go decision is performed by replacing unknown population-level quantities in the power function with what is observed from a literature review or what is observed in phase two. Many authors and practitioners view this as an assumed value of power and offer the Bayesian quantity probability of success or assurance as an alternative. The claim is by averaging over a prior or posterior distribution, probability of success transcends power by capturing the uncertainty around the unknown true treatment effect and any other population-level parameters. We use confidence distributions to frame both the probability of success calculation and the typical power calculation as merely producing two different point estimates of power. We demonstrate that Go/No-Go decisions based on either point estimate of power do not adequately quantify and control the risk involved, and instead we argue for Go/No-Go decisions that utilize inference on power for better risk management and decision making. This inference on power can be derived and displayed using confidence distributions