Determining optimal sample sizes for multistage adaptive randomized clinical trials from an industry perspective using value of information methods

Clin Trials: December 21, 2012 

Background  Most often, sample size determinations for randomized clinical trials are based on frequentist approaches that depend on somewhat arbitrarily chosen factors, such as type I and II error probabilities and the smallest clinically important difference. As an alternative, many authors have proposed decision-theoretic (full Bayesian) approaches, often referred to as value of information methods that attempt to determine the sample size that maximizes the difference between the trial’s expected utility and its expected cost, referred to as the expected net gain. Taking an industry perspective, Willan proposes a solution in which the trial’s utility is the increase in expected profit. Furthermore, Willan and Kowgier, taking a societal perspective, show that multistage designs can increase expected net gain.

Purpose  The purpose of this article is to determine the optimal sample size using value of information methods for industry-based, multistage adaptive randomized clinical trials, and to demonstrate the increase in expected net gain realized. At the end of each stage, the trial’s sponsor must decide between three actions: continue to the next stage, stop the trial and seek regulatory approval, or stop the trial and abandon the drug. Read More