Journal
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Volume -, Issue -, Pages -Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/01621459.2023.2183131
Keywords
Causal inference; Delivery hospital; Marginal structural model; Time-varying endogeneity; Unmeasured confounding
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This article introduces Marginal Structural Models (MSMs), a type of counterfactual models for studying the joint effects of time-varying treatment regimes in complex longitudinal studies subject to time-varying confounding. The author establishes identification of MSM parameters under a Sequential Randomization Assumption (SRA), which assumes no unmeasured confounding of treatment assignment over time. When the Sequential Randomization Assumption fails, we propose using a time-varying instrumental variable to identify the parameters of a subset called Marginal Structural Mean Models (MSMMs). The article presents a weighted estimator and evaluates its performance through simulation studies, applying it to investigate the effect of delivery hospital type on neonatal survival probability.
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.
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