Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, Utah, USA.
Department of Biostatistics, Brown University, Providence, Rhode Island, USA.
Biometrics. 2022 Jun;78(2):649-659. doi: 10.1111/biom.13455. Epub 2021 Apr 6.
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 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.
在本文中，我们提出了一种方法，用于对随机试验进行全局敏感性分析，其中在随机分组后预设的时间点上对参与者进行二分类结局的收集，并且这些结局可能以非单调的方式缺失。我们引入了一类缺失数据假设，由敏感性参数索引，这些假设以 Robins（Statistics in Medicine，1997）提出的缺失不是随机的假设为基础。对于类中的每个假设，我们确定从观察到的数据的分布中可以识别结局的联合分布。我们的估计程序使用插件原理，其中使用随机森林来估计观察到的数据的分布。我们为我们的估计程序建立了渐近性质。我们在一项旨在通过每周两次检测尿液样本来评估减少物质使用的新方法的随机试验背景下说明了我们的方法，该方法用于评估进入门诊成瘾治疗的患者。我们在现实的模拟研究中评估了我们方法的有限样本性质。我们的方法已经在一个名为 slabm 的 R 包中实现。
