A Bayesian sensitivity model for intention-to-treat analysis on binary outcomes with dropouts
A Bayesian sensitivity model for intention-to-treat analysis on binary outcomes with dropouts
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DOI：
10.1002/sim.3494
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发表时间：
2009-02-15
影响因子：
2
通讯作者：
Julius, Stevo
中科院分区：
文献类型：
--
作者：
Kaciroti, Niko A.;Schork, M. Anthony;Julius, Stevo
意向治疗(ITT)分析是随机临床试验中常用的方法。然而，使用ITT分析带来了一个挑战：如何处理辍学的受试者。在这里，我们关注的是石油随机试验，其中主要结果是一个二元终点。有几种方法可用于将辍学对象纳入ITT分析，主要是在解除研究盲目之前选择的。这些方法减少了由于打破随机化代码而产生的潜在偏差。然而，结果的有效性将高度依赖于石油不可测试的假设；关于退出机制的假设。因此，评估不同缺失数据机制的结果的敏感性是很重要的。在此，我们提出了一种适用于不同类型的缺失数据机制的贝叶斯模式混合模型，用于具有丢弃的二元结果的ITT分析。我们引入了新的参数化法来辨识模型，并将其用于灵敏度分析。参数化定义为退出研究的受试者和完成研究的受试者之间所有终点的赔率比。这种参数化是直观和容易使用的不良敏感性分析；它还结合了大多数可用的方法作为特例。该模型在高血压防治试验中得到应用。版权所有(C)2008 John Wiley&Sons。LTD.
Intention-to-treat (ITT) analysis is commonly used in randomized clinical trials. However, the use of ITT analysis presents a challenge: how to deal with Subjects who drop Out. Here we focus oil randomized trials where the primary outcome is a binary endpoint. Several approaches are available for including the dropout subject ill the ITT analysis, mainly chosen prior to unblinding the Study. These approaches reduce the potential bias due to breaking the randomization code.. However, the validity of the results will highly depend oil untestable assumptions; about the dropout mechanism. Thus, it is important to evaluate the sensitivity of the results across different missing-data mechanisms. We propose here a Bayesian pattern-mixture model for ITT analysis of binary outcomes with dropouts that applies over different types of missing-data mechanisms. We introduce it new parameterization to identify the model, which is then used for sensitivity analysis. The parameterization is defined as the odds ratio of having all endpoint between the Subjects who dropped Out and those who completed the study. Such parameterization is intuitive and easy 10 use ill sensitivity analysis; it also incorporates most of the available methods as special cases. The model is applied to TRial Of Preventing HYpertension. Copyright (C) 2008 John Wiley & Sons. Ltd.