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Activity Number: 295 - Causal, Robust, and Machine Learning for Survival Outcomes
Type: Invited
Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
Sponsor: Lifetime Data Science Section
Abstract #317003
Title: Instrumental Variable Estimation of Marginal Structural Cox Model for Time-Varying Treatments
Author(s): Eric Tchetgen Tchetgen*
Companies: University of Pennsylvania

Robins (1998) introduced marginal structural models (MSMs), a general class of counterfactual models for the joint effects of time-varying treatments in complex longitudinal studies subject to time-varying confounding. Robins (1998) established the identification of MSM parameters under a sequential randomization assumption, which rules out unmeasured confounding of treatment assignment over time. Cox MSM is one of the most popular MSMs to evaluate the causal effect of time-varying treatments on a censored failure time outcome. In this paper, we establish sufficient conditions for identification of Cox MSM parameters with the aid of a time-varying instrumental variable, when sequential randomization fails to hold due to unmeasured confounding. Our instrumental variable identification condition rules out any interaction between an unmeasured confounder and the instrumental variable in its additive effects on the treatment process, the longitudinal generalization of the identifying condition of Wang & Tchetgen Tchetgen (2018). We describe a large class of weighted estimating equations that give rise to consistent and asymptotically normal estimators of the Cox MSM, thereby extending

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