Bayesian predictive approach to interim monitoring in clinical trials
Bayesian predictive approach to interim monitoring in clinical trials
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DOI：
10.1002/sim.2204
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发表时间：
2006-07-15
影响因子：
2
通讯作者：
Wang, MD
中科院分区：
文献类型：
--
作者：
Dmitrienko, A;Wang, MD
本文综述了贝叶斯监测临床试验数据的策略。其重点是基于在给定观察数据的研究计划结束时观察到临床显著结局的预测概率的贝叶斯随机缩减方法。所提出的方法适用于连续，正态分布和二元终点的临床试验中的有效性和无效性停止规则。由此产生的停止规则的先验分布的选择的敏感性进行了检查，并讨论了选择治疗效果的先验分布的指导方针。贝叶斯预测方法相比，频率论（条件功率）和混合贝叶斯频率论（预测功率）的方法。本文中讨论的临时监测策略使用来自小型概念验证研究和大型死亡率试验的示例进行说明。版权所有（c）2005年约翰威利父子有限公司。
This paper reviews Bayesian strategies for monitoring clinical trial data. It focuses on a Bayesian stochastic curtailment method based on the predictive probability of observing a clinically significant outcome at the scheduled end of the study given the observed data. The proposed method is applied to derive efficacy and futility stopping rules in clinical trials with continuous, normally distributed and binary endpoints. The sensitivity of the resulting stopping rules to the choice of prior distributions is examined and guidelines for choosing a prior distribution of the treatment effect are discussed. The Bayesian predictive approach is compared to the frequentist (conditional power) and mixed Bayesian-frequentist (predictive power) approaches. The interim monitoring strategies discussed in the paper are illustrated using examples from a small proof-of-concept study-and a large mortality trial. Copyright (c) 2005 John Wiley & Sons, Ltd.