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.
翻译:在随机控制的试验中,治疗转换(也称为污染或交叉)发生于最初指派给一个治疗手臂的病人在后续跟踪过程中对另一个手臂进行改变时,治疗转换(也称为污染或交叉)发生于最初指派给一个治疗手臂的病人在另一个手臂在另一个手臂上发生改变时,忽视治疗转换可能大大偏向于对治疗功效或安全的评价。为了说明治疗转换情况,利用最初的随机分配作为IV的方法,工具变数(IV)作为自然调整方法,因为这些方法允许依赖性治疗转换,可能由于基本预测而产生。然而,四种方法的“排除限制”假设要求最初随机调整对结果不产生直接影响,这种假设仍然值得怀疑,特别是公开标签试验。我们提议一个强有力的工具变量估计器绕过这种洞穴。我们从我们提议的估算器中获取大量抽样特性,同时使用推断工具。我们进行了广泛的模拟,以审查我们的估计器及其相关推断工具的有限性性性性性能。RCRAN可自由提供“ivsaclim's ”软件包件,要求最初随机执行所有拟议方法,对结果不产生直接影响。我们应用该估计性估算性评估器来评估Ncleclecleadtrasistratimtradistratimtal Indistrationsinstrital 的Nastistrital