Group Sequential Clinical Trials: A Classical Evaluation of Bayesian Decision-Theoretic Designs
组序贯临床试验：贝叶斯决策论设计的经典统计评价AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究临床组序贯检验与决策成本，治疗比较本身不足以归入因果推断细分。
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组序贯临床试验：贝叶斯决策论设计的经典统计评价
- 研究问题
- 比较两种治疗的二元结局时，如何按总成本构造贝叶斯序贯停止与决策规则，并评价其频率学派错误率和平均样本量？
- 方法与思路
- 作者为两组成功概率设独立贝塔先验，以样本量加终端损失定义总成本，用逆向归纳求规则，再以蒙特卡洛比较经典设计。
- 材料陈述的贡献
- 摘要称，贝叶斯设计平均成本较低；将终端损失调至产生相近频率学派错误率时，其平均样本量通常也小于所比较经典设计。
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- 应核对原PDF中的假设方向、最小效应与概率下标，以及终端损失设定、停止边界和错误率匹配的具体比较方式。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；变量上下标排版及假设关系需核对原PDF，损失敏感性和数值结果未说明。
原文摘要
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.