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.
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.
A model for expected total profit is proposed that includes consideration of per-patient profit, disease incidence, time horizon, trial duration, market share, and the relationship between trial results and probability of regulatory approval. The proposed method is extended to include multistage designs with a solution provided for a two-stage design. An example is given.
Significant increases in the expected net gain are realized by using multistage designs.
The complexity of the solutions increases with the number of stages, although far simpler near-optimal solutions exist. The method relies on the central limit theorem, assuming that the sample size is sufficiently large so that the relevant statistics are normally distributed.
From a value of information perspective, the use of multistage designs in industry trials leads to significant gains in the expected net gain.