A common objective of biomedical cohort studies is assessing the effect of a time-varying treatment or exposure on a survival time. In the presence of time-varying confounders, marginal structural models fit using inverse probability weighting can be employed to obtain a consistent and asymptotically normal estimator of the causal effect of a time-varying treatment. This article considers estimation of parameters in the semiparametric marginal structural Cox model (MSCM) from a case-cohort study. Case-cohort sampling entails assembling covariate histories only for cases and a random subcohort, which can be cost effective, particularly in large cohort studies with low outcome rates. Following Cole et al. (2012), we consider estimating the causal hazard ratio from a MSCM by maximizing a weighted-pseudo-partial-likelihood. The estimator is shown to be consistent and asymptotically normal under certain regularity conditions. Finite sample performance of the proposed estimator is evaluated in a simulation study. In the corresponding supplementary document, computation of the estimator using standard survival analysis software is presented.

中文翻译：

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具有案例队列抽样的边际结构 cox 模型

生物医学队列研究的一个共同目标是评估随时间变化的治疗或暴露对生存时间的影响。在存在时变混杂因素的情况下，可以采用使用逆概率加权拟合的边际结构模型来获得时变处理因果效应的一致且渐近正态估计量。本文考虑了来自案例队列研究的半参数边缘结构 Cox 模型 (MSCM) 中的参数估计。病例队列抽样需要为病例和随机子队列组装协变量历史，这可能具有成本效益，特别是在结果率低的大型队列研究中。继科尔等人之后。(2012)，我们考虑通过最大化加权伪偏可能性来估计来自 MSCM 的因果风险比。在某些规律性条件下，估计量被证明是一致且渐近正态的。在模拟研究中评估了所提议估计器的有限样本性能。在相应的补充文件中，介绍了使用标准生存分析软件计算估计量。