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关注这篇论文的作者
Zhangyi He
Feng Yu
Suzie Cro
Laurent Billot

贝叶斯组序贯设计中从激进到保守的提前停止

研究问题
贝叶斯组序贯试验如何在控制整体第一类错误的同时减少过早停止,并使早期决策更接近保守的频率学派边界?
方法与思路
作者提出两阶段后验概率阈值,以及在中期分析使用预测概率的方案;两者均增加一个调节参数并进行校准。
材料陈述的贡献
摘要称在 HYPRESS 场景中,两种方案较常规贝叶斯设计具有更高功效,早期错误率支出形态接近 O’Brien–Fleming。
阅读关注点
应核对两种停止规则、先验与调节参数的设定、第一类错误的校准范围,以及功效比较是否使用相同设计约束。
阅读边界
这是 arXiv 预印本,不能据此认定已同行评审或获期刊接收;摘要未给出校准细节,场景中的功效结果不能直接推广。

原文摘要

Group sequential designs (GSDs) are widely used in confirmatory trials to allow interim monitoring while preserving control of the type I error rate. In the frequentist framework, O'Brien-Fleming-type stopping boundaries dominate practice because they impose highly conservative early stopping while allowing more liberal decisions as information accumulates. Bayesian GSDs, in contrast, are most often implemented using fixed posterior probability thresholds applied uniformly at all analyses. While such designs can be calibrated to control the overall type I error rate, they do not penalise early analyses and can therefore lead to substantially more aggressive early stopping. Such behaviour can risk premature conclusions and inflation of treatment effect estimates, raising concerns for confirmatory trials. We introduce two practically implementable refinements that restore conservative early stopping in Bayesian GSDs. The first introduces a two-phase structure for posterior probability thresholds, applying more stringent criteria in the early phase of the trial and relaxing them later to preserve power. The second replaces posterior probability monitoring at interim looks with predictive probability criteria, which naturally account for uncertainty in future data and therefore suppress premature stopping. Both strategies require only one additional tuning parameter and can be efficiently calibrated. In the HYPRESS setting, both approaches achieve higher power than the conventional Bayesian design while producing alpha-spending profiles closely aligned with O'Brien-Fleming-type behaviour at early looks. These refinements provide a principled and tractable way to align Bayesian GSDs with accepted frequentist practice and regulatory expectations, supporting their robust application in confirmatory trials.

笔记与分组