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Activity Number: 655 - Improving Power and Generalizability in Causal Effect Estimation Using Multicenter and Network Designs
Type: Topic Contributed
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #329565
Title: Marginal Structural Models to Estimate the Effects of Time-Varying Treatments on Clustered Outcomes in the Presence of Interference
Author(s): Alisa Stephens-Shields* and Jiwei He and Marshall Joffe
Companies: University of Pennsylvania and US Food and Drug Administration and University of Pennsylvania
Keywords: marginal structural models; clustered data; interference; IPW; time-varying treatment

Marginal structural models (MSMs) are a class of causal models useful for characterizing the effect of treatment in the presence of time-varying confounding. They are more widely used than structural nested models (SNMs), partly because these models are easier to understand and to implement. We extend MSMs to situations with clustered observations with unit- and cluster-level treatment and introduce an appropriate inferential method. We consider how to formulate models with cluster-level and unit-level treatments. For unit-level treatments, we consider cases with and without interference. We also consider the use of unit-specific inverse probability weights (IPWs) and certain working correlation structures to improve the efficiency of estimators in some situations. We apply our method to different scenarios including 2 or 3 units per cluster and a mixture of larger clusters. Simulation examples and data from the treatment arm of a glaucoma clinical trial were used to illustrate our method.

Authors who are presenting talks have a * after their name.

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