JSM 2005 - Toronto

Abstract #302508

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 214
Type: Invited
Date/Time: Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #302508
Title: History-Adjusted Marginal Structural Models for the Estimation of Causal Effect Modification by Time-Dependent Covariates
Author(s): Maya L. Petersen*+ and Mark van der Laan
Companies: University of California, Berkeley and University of California, Berkeley
Address: 1351 Guerrero St., San Francisco, CA, 94110, USA
Keywords: causal inference ; dynamic treatment regime ; inverse probability of treatment weighted estimation ; HIV ; antiretroviral
Abstract:

Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a treatment, particularly in the context of longitudinal data structures. These models, introduced by Robins, model the marginal distributions of treatment-specific counterfactual outcomes, possibly conditional on a subset of the baseline covariates. However, standard MSM cannot incorporate modification of treatment effects by time-varying covariates. In the context of clinical decisionmaking, such time-varying effect modifiers are often of considerable interest, as they are used in practice to guide treatment decisions for an individual. In this talk, I will introduce a generalization of marginal structural models, which we call history-adjusted marginal structural models (HA-MSM). These models allow estimation of adjusted causal effects of treatment, given the observed past, and are therefore more suitable for making treatment decisions at the individual level and for identification of time-dependent effect modifiers. In addition, HA-MSM identify a particular optimal decision rule for assigning treatment at each time point based on a subject's measured covariates up untill that time point.

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Revised March 2005