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arXiv · 2004.11769

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

Haben Michael·Yifan Cui·Scott Lorch·Eric Tchetgen Tchetgen
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Abstract

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.

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Haben Michael, Yifan Cui, Scott Lorch, Eric Tchetgen Tchetgen. 2020-04-24. Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment. https://arxiv.org/abs/2004.11769

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