In this paper, we present a method for conducting global sensitivity analysis of randomized trials in which binary outcomes are scheduled to be collected on participants at prespecified points in time after randomization and these outcomes may be missing in a nonmonotone fashion. We introduce a class of missing data assumptions, indexed by sensitivity parameters, which are anchored around the missing not at random assumption introduced by Robins (Statistics in Medicine, 1997). For each assumption in the class, we establish that the joint distribution of the outcomes is identifiable from the distribution of the observed data. Our estimation procedure uses the plug-in principle, where the distribution of the observed data is estimated using random forests. We establish n−−√$\sqrt{n}$$\sqrt{n}$ asymptotic properties for our estimation procedure. We illustrate our methodology in the context of a randomized trial designed to evaluate a new approach to reducing substance use, assessed by testing urine samples twice weekly, among patients entering outpatient addiction treatment. We evaluate the finite sample properties of our method in a realistic simulation study. Our methods have been implemented in an R package entitled slabm.

中文翻译：

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非单调缺失二元结果随机试验的全局敏感性分析：在物质使用障碍研究中的应用

在本文中，我们提出了一种对随机试验进行全局敏感性分析的方法，其中计划在随机化后的预先指定的时间点收集参与者的二元结果，并且这些结果可能会以非单调方式丢失。我们引入了一类缺失数据假设，由敏感性参数索引，这些假设基于 Robins 引入的缺失非随机假设（医学统计，1997）。对于类中的每个假设，我们确定结果的联合分布可以从观测数据的分布中识别出来。我们的估计过程使用插件原理，其中使用随机森林估计观测数据的分布。我们建立n--√$\sqrt{n}$$\sqrt{n}$我们的估计过程的渐近性质。我们在一项随机试验的背景下说明了我们的方法，该试验旨在评估一种减少药物使用的新方法，通过每周两次测试接受门诊成瘾治疗的患者的尿液样本进行评估。我们在现实模拟研究中评估了我们方法的有限样本属性。我们的方法已在名为s labm 的R包中实现。