论文标题
随时间变化的边际结构平均模型的仪器变量估计
Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment
论文作者
论文摘要
Robins 1997引入了边际结构模型（MSMS），这是一类反事实模型，用于在复杂的纵向研究中随时间变化的治疗方案的联合影响。在他的工作中，MSM参数的识别是在一个顺序的随机假设（SRA）下建立的，该假设排除了随着时间的流逝，无法测量治疗分配的混杂。当顺序随机化由于无法测量的混杂而无法保持时，我们考虑了足够的条件来识别子类，边缘结构均值模型（MSMMS）的参数，而是使用时间变化的仪器变量。我们的识别条件要求没有未观察到的混杂因素可以预测随时间变化的处理的合规性类型。我们描述了一个简单的加权估计器，并在仿真研究中检查其有限样本的特性。我们应用拟议的估计器来检查分娩医院对新生儿生存概率的影响。
Robins 1997 introduced marginal structural models (MSMs), a general class of counterfactual models for the joint effects of time-varying treatment regimes in complex longitudinal studies subject to time-varying confounding. In his work, identification of MSM parameters is established under a sequential randomization assumption (SRA), which rules out unmeasured confounding of treatment assignment over time. We consider sufficient conditions for identification of the parameters of a subclass, Marginal Structural Mean Models (MSMMs), when sequential randomization fails to hold due to unmeasured confounding, using instead a time-varying instrumental variable. Our identification conditions require that no unobserved confounder predicts compliance type for the time-varying treatment. We describe a simple weighted estimator and examine its finite-sample properties in a simulation study. We apply the proposed estimator to examine the effect of delivery hospital on neonatal survival probability.
