Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment

• 成果类型: Article
• 署名作者: Michael, Haben; Cui, Yifan; Lorch, Scott A.; Tchetgen, Eric Tchetgen J.
• 署名单位: University of Massachusetts System; University of Massachusetts Amherst; Zhejiang University; University of Pennsylvania; University of Pennsylvania
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2023.2183131
• 发表日期: 2024
• 页码: 1240-1251
• 关键词: identification; trials

摘要

Robins 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 type on neonatal survival probability. for this article are available online.