Zheng Cheng, Dai Ran, Hari Parameswaran N, Zhang Mei-Jie
Joseph. J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, U.S.A.
Department of Statistics, University of Chicago, Chicago, IL, U.S.A.
Stat Med. 2017 Apr 15;36(8):1240-1255. doi: 10.1002/sim.7205. Epub 2017 Jan 8.
In this paper, we discuss causal inference on the efficacy of a treatment or medication on a time-to-event outcome with competing risks. Although the treatment group can be randomized, there can be confoundings between the compliance and the outcome. Unmeasured confoundings may exist even after adjustment for measured covariates. Instrumental variable methods are commonly used to yield consistent estimations of causal parameters in the presence of unmeasured confoundings. On the basis of a semiparametric additive hazard model for the subdistribution hazard, we propose an instrumental variable estimator to yield consistent estimation of efficacy in the presence of unmeasured confoundings for competing risk settings. We derived the asymptotic properties for the proposed estimator. The estimator is shown to be well performed under finite sample size according to simulation results. We applied our method to a real transplant data example and showed that the unmeasured confoundings lead to significant bias in the estimation of the effect (about 50% attenuated). Copyright © 2017 John Wiley & Sons, Ltd.
在本文中，我们讨论了在存在竞争风险的情况下，关于治疗或药物对事件发生时间结局的疗效的因果推断。尽管治疗组可以进行随机分组，但依从性和结局之间可能存在混杂因素。即使在对已测量的协变量进行调整之后，仍可能存在未测量的混杂因素。在存在未测量的混杂因素的情况下，工具变量法通常用于对因果参数进行一致估计。基于次分布风险的半参数加性风险模型，我们提出了一种工具变量估计量，以在存在竞争风险设置的未测量混杂因素的情况下，对疗效进行一致估计。我们推导了所提出估计量的渐近性质。根据模拟结果，该估计量在有限样本量下表现良好。我们将我们的方法应用于一个实际的移植数据示例，并表明未测量的混杂因素会导致效应估计中的显著偏差（约衰减50%）。版权所有© 2017约翰威立父子有限公司。
