A major concern in any observational study is unmeasured confounding of the relationship between a treatment and outcome of interest. Instrumental variable (IV) analysis methods are able to control for unmeasured confounding. However, IV analysis methods developed for censored time-to-event data tend to rely on assumptions that may not be reasonable in many practical applications, making them unsuitable for use in observational studies. In this report, we develop weighted estimators of the complier average causal effect (CACE) on the restricted mean survival time in the overall population as well as in an evenly matchable population (CACE-m). Our method is able to accommodate instrument-outcome confounding and adjust for covariate-dependent censoring, making it particularly suited for causal inference from observational studies. We establish the asymptotic properties and derive easily implementable asymptotic variance estimators for the proposed estimators. Through simulation studies, we show that the proposed estimators tend to be more efficient than instrument propensity score matching-based estimators or IPIW estimators. We apply our method to compare dialytic modality-specific survival for end stage renal disease patients using data from the U.S. Renal Data System.

中文翻译：

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使用观察到的仪器-结果混杂因素对有限平均生存时间的编译者平均因果效应的加权估计


任何观察性研究中的一个主要问题是治疗与感兴趣的结果之间的关系无法衡量的混杂。工具变量（IV）分析方法能够控制未测量的混杂因素。然而，为审查事件时间数据开发的 IV 分析方法往往依赖于在许多实际应用中可能不合理的假设，使得它们不适合在观察性研究中使用。在本报告中，我们开发了总体人群以及均匀匹配人群 (CACE-m) 中受限平均生存时间的编译者平均因果效应 (CACE) 的加权估计量。我们的方法能够适应仪器结果混杂并调整协变量相关的审查，使其特别适合观察性研究的因果推断。我们建立了渐近性质，并为所提出的估计量导出了易于实现的渐近方差估计量。通过模拟研究，我们表明所提出的估计器往往比基于工具倾向得分匹配的估计器或 IPIW 估计器更有效。我们使用美国肾脏数据系统的数据应用我们的方法来比较终末期肾病患者的透析方式特异性生存率。