论文标题
稳健的仪器变量方法会说明开放标签随机对照试验中的治疗转换的方法
A Robust Instrumental Variable Method Accounting for Treatment Switching in Open-Label Randomized Controlled Trials
论文作者
论文摘要
在一项随机对照试验中，当最初分配给一个治疗臂的患者在随访过程中最初分配给一个治疗臂的患者会变化时，就会发生治疗转换（也称为污染或交叉）。俯瞰治疗转换可能会大大偏向于评估治疗功效或安全性。为了说明治疗转换，通过利用初始随机分配作为静脉注射的仪器变量（IV）方法是自然调整方法，因为它们允许依赖的治疗切换可能是由于潜在的预后而引起的。但是，IV方法的``排除限制''假设要求初始随机化对结果没有直接影响，尤其是对于开放标签试验而言仍然值得怀疑。我们提出了一个强大的仪器变量估计器，以规避这种警告。我们得出了提出的估计器的大样本特性以及推理工具。我们进行广泛的模拟，以检查估计器及其相关推论工具的有限性能。 R cran可以免费获得实施所有建议方法的R软件包``ivsacim''。我们应用估计量来评估核苷逆转录酶抑制剂（NRTIS）对包括或省略NRTIS试验的优化治疗中的安全结果的治疗效果。
In a randomized controlled trial, treatment switching (also called contamination or crossover) occurs when a patient initially assigned to one treatment arm changes to another arm during the course of follow-up. Overlooking treatment switching might substantially bias the evaluation of treatment efficacy or safety. To account for treatment switching, instrumental variable (IV) methods by leveraging the initial randomized assignment as an IV serve as natural adjustment methods because they allow dependent treatment switching possibly due to underlying prognoses. However, the ``exclusion restriction'' assumption for IV methods, which requires the initial randomization to have no direct effect on the outcome, remains questionable, especially for open-label trials. We propose a robust instrumental variable estimator circumventing such a caveat. We derive large-sample properties of our proposed estimator, along with inferential tools. We conduct extensive simulations to examine the finite performance of our estimator and its associated inferential tools. An R package ``ivsacim'' implementing all proposed methods is freely available on R CRAN. We apply the estimator to evaluate the treatment effect of Nucleoside Reverse Transcriptase Inhibitors (NRTIs) on a safety outcome in the Optimized Treatment That Includes or Omits NRTIs trial.
