Wei Bo, Peng Limin, Zhang Mei-Jie, Fine Jason P
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, USA.
Department of Biostatistics, Medical College of Wisconsin.
J R Stat Soc Series B Stat Methodol. 2021 Jul;83(3):559-578. doi: 10.1111/rssb.12431. Epub 2021 Jul 1.
The causal effect of a treatment is of fundamental interest in the social, biological, and health sciences. Instrumental variable (IV) methods are commonly used to determine causal treatment effects in the presence of unmeasured confounding. In this work, we study a new binary IV framework with randomly censored outcomes where we propose to quantify the causal treatment effect by the concept of complier quantile causal effect (CQCE). The CQCE is identifiable under weaker conditions than the complier average causal effect when outcomes are subject to censoring, and it can provide useful insight into the dynamics of the causal treatment effect. Employing the special characteristic of the binary IV and adapting the principle of conditional score, we uncover a simple weighting scheme that can be incorporated into the standard censored quantile regression procedure to estimate CQCE. We develop robust nonparametric estimation of the derived weights in the first stage, which permits stable implementation of the second stage estimation based on existing software. We establish rigorous asymptotic properties for the proposed estimator, and confirm its validity and satisfactory finite-sample performance via extensive simulations. The proposed method is applied to a bone marrow transplant dataset to evaluate the causal effect of rituximab in diffuse large B-cell lymphoma patients.
治疗的因果效应在社会科学、生物科学和健康科学中具有根本重要性。在存在未测量混杂因素的情况下，工具变量（IV）方法通常用于确定因果治疗效应。在这项工作中，我们研究了一种新的具有随机删失结果的二元IV框架，在此框架下我们建议通过依从者分位数因果效应（CQCE）的概念来量化因果治疗效应。当结果受到删失时，CQCE在比依从者平均因果效应更弱的条件下是可识别的，并且它可以为因果治疗效应的动态变化提供有用的见解。利用二元IV的特殊特性并采用条件得分原理，我们发现了一种简单的加权方案，该方案可以纳入标准的删失分位数回归程序中来估计CQCE。我们在第一阶段对导出的权重进行稳健的非参数估计，这使得基于现有软件能够稳定地实施第二阶段的估计。我们为所提出的估计量建立了严格的渐近性质，并通过广泛的模拟证实了其有效性和令人满意的有限样本性能。所提出的方法应用于一个骨髓移植数据集，以评估利妥昔单抗对弥漫性大B细胞淋巴瘤患者的因果效应。
