Using instrumental variables to estimate a Cox's proportional hazards regression subject to additive confounding.
Using instrumental variables to estimate a Cox's proportional hazards regression subject to additive confounding.
复制标题
使用工具变量来估计COX的比例危害回归，但会受到添加混杂的影响。
DOI：
10.1007/s10742-014-0117-x
复制
发表时间：
2014-06
影响因子：
1.5
通讯作者：
O'Malley, A James
中科院分区：
文献类型：
--
作者：
MacKenzie, Todd A;Tosteson, Tor D;Morden, Nancy E;Stukel, Therese A;O'Malley, A James
治疗效果的估计是医学统计学的主要目标之一。基于观察性研究的估计可能会受到混杂因素的影响。控制混杂偏倚的统计方法包括回归调整、倾向评分和逆概率加权估计。这些方法要求在数据中记录所有混杂因素。工具变量法（IV）可以消除观察性研究中的偏倚，即使在缺乏混杂因素信息的情况下。我们提出了一种方法整合静脉注射的框架内的考克斯的比例风险模型，并证明了条件下，它恢复的因果关系的治疗效果。该方法是基于近似正交的工具与未观察到的混杂因素之间的风险。我们推导出一个估计量作为估计方程的解，该估计方程类似于偏似然的得分方程，其方式与传统的IV估计量类似于正态方程的方式大致相同。为了证明这个IV估计的考克斯模型，我们进行模拟，以评估其操作特性。最后，我们将估计量应用于冠状动脉导管插入术对生存率影响的观察性研究。
The estimation of treatment effects is one of the primary goals of statistics in medicine. Estimation based on observational studies is subject to confounding. Statistical methods for controlling bias due to confounding include regression adjustment, propensity scores and inverse probability weighted estimators. These methods require that all confounders are recorded in the data. The method of instrumental variables (IVs) can eliminate bias in observational studies even in the absence of information on confounders. We propose a method for integrating IVs within the framework of Cox's proportional hazards model and demonstrate the conditions under which it recovers the causal effect of treatment. The methodology is based on the approximate orthogonality of an instrument with unobserved confounders among those at risk. We derive an estimator as the solution to an estimating equation that resembles the score equation of the partial likelihood in much the same way as the traditional IV estimator resembles the normal equations. To justify this IV estimator for a Cox model we perform simulations to evaluate its operating characteristics. Finally, we apply the estimator to an observational study of the effect of coronary catheterization on survival.