This talk considers the problem of assessing causal effect moderation in longitudinal settings in which treatment (or exposure) is time-varying and so are the covariates said to moderate its effect. Intermediate causal effects that describe time-varying causal effects of treatment conditional on past covariate history are introduced and considered as part of Robins' Structural Nested Mean Model. Two estimators of the intermediate causal effects, and their standard errors, are presented and discussed: The first is a proposed 2-Stage Regression Estimator, which can be carried out using standard regression software. The second is Robins' G-Estimator. The results of a small simulation study that begins to shed light on the small versus large sample performance of the estimators, and on the bias-variance trade-off between the two estimators are presented. If time permits, the results of an illustrative analysis of the methodology using longitudinal data from a depression study are presented.This is joint work with Thomas Ten Have (Univ of Pennsylvania) and Susan Murphy (Univ of Michigan).
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