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Robins 1997 introduced marginal structural models (MSMs), a general class ofcounterfactual models for the joint effects of time-varying treatment regimesin complex longitudinal studies subject to time-varying confounding. In hiswork, identification of MSM parameters is established under a sequentialrandomization assumption (SRA), which rules out unmeasured confounding oftreatment assignment over time. We consider sufficient conditions foridentification of the parameters of a subclass, Marginal Structural Mean Models(MSMMs), when sequential randomization fails to hold due to unmeasuredconfounding, using instead a time-varying instrumental variable. Ouridentification conditions require that no unobserved confounder predictscompliance type for the time-varying treatment. We describe a simple weightedestimator and examine its finite-sample properties in a simulation study. Weapply the proposed estimator to examine the effect of delivery hospital onneonatal survival probability.

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