Group Sequential Clinical Trials: A Classical Evaluation of Bayesian Decision-Theoretic Designs
开发并评估了比较两种二元结果治疗的贝叶斯决策理论设计，通过蒙特卡洛模拟与经典分组序贯设计对比，发现贝叶斯设计在相似错误率下平均样本量更小。
Abstract Bayesian decision-theoretic designs for a clinical trial comparing two treatments for a disease with binary outcomes are developed and evaluated. The probability of successful outcome with treatment i is denoted by pi , i = 1, 2, and prior knowledge regarding each pi is assumed to follow a beta distribution. The pi are assumed to be independent. To facilitate comparison with frequentist clinical trial designs, we take a hypothesis-testing approach. The null hypothesis is δ < δ 0, and the alternative hypothesis is δ > 0, where δ 0 is the minimum treatment effect sought by the trial and δ = p 2 - p 1 is the true treatment difference. We use a simple terminal loss function reflecting the hypothesis-testing goal of the trial, and the total cost of the trial is the final sample size plus the terminal loss function. The stopping and decision rules that minimize the expectation of the total cost are determined by backward induction. Monte Carlo simulation is used to compare Bayesian and frequentist error rates and mean sample sizes of these Bayesian designs with one-tailed classical group-sequential designs of Pocock and O'Brien-Fleming. As expected, the Bayesian decision-theoretic designs have smaller mean costs than the classical designs. More surprising, when the magnitude of the terminal loss function is chosen to yield frequentist error rates similar to those for classical designs, the mean sample sizes of the Bayesian designs are usually smaller. Key Words: Bayesian decision theoryClinical trialsGroup-sequential methodsInterim analysis