American Journal of Epidemiology Advance Access originally published online on September 17, 2007
American Journal of Epidemiology 2007 166(9):985-993; doi:10.1093/aje/kwm232
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ORIGINAL CONTRIBUTIONS |
History-adjusted Marginal Structural Models for Estimating Time-varying Effect Modification
1 Division of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA
2 Department of Medicine, San Francisco General Hospital, University of California, San Francisco, San Francisco, CA
Correspondence to Dr. Maya L. Petersen, Division of Biostatistics, School of Public Health, University of California, Berkeley, Earl Warren Hall #7360, Berkeley, CA 94720-7360 (e-mail: mayaliv{at}gmail.com).
Received for publication December 2, 2005. Accepted for publication September 7, 2006.
Much of epidemiology and clinical medicine is focused on estimating the effects of treatments or interventions administered over time. In such settings of longitudinal treatment, time-dependent confounding is often an important source of bias. Marginal structural models (MSMs) are a powerful tool for estimating the causal effect of a treatment using observational data, particularly when time-dependent confounding is present. In recent statistical work, van der Laan et al. presented a generalized form of MSMs called "history-adjusted" MSMs (Int J Biostat 2005;1:article 4). Unlike standard MSMs, history-adjusted MSMs can be used to estimate modification of treatment effects by time-varying covariates. Estimation of time-dependent causal effect modification is frequently of great practical relevance. For example, clinical researchers are often interested in how the prognostic significance of a biomarker for treatment response can change over time. This article provides a practical introduction to the implementation and interpretation of history-adjusted MSMs. The method is illustrated using a clinical question drawn from the treatment of human immunodeficiency virus infection. Observational cohort data from San Francisco, California, collected between 2000 and 2004, are used to estimate the effect of time until switching antiretroviral therapy regimens among patients receiving a nonsuppressive regimen and how this effect differs depending on CD4-positive T-lymphocyte count.
antiretroviral therapy, highly active; causality; confounding factors (epidemiology); HIV; longitudinal studies; observational data; structural model; time-dependent covariate
Abbreviations: HA-MSM, history-adjusted marginal structural model; HIV, human immunodeficiency virus; IPTW, inverse-probability-of-treatment-weighted; MSM, marginal structural model; SCOPE, Study on the Consequences of the Protease Inhibitor Era
Editor's note: An invited commentary on this article appears on page 994, and the authors' response appears on page 1003.
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Related articles in Am. J. Epidemiol.:
- Petersen et al. Respond to "Effect Modification by Time-varying Covariates"
- Maya L. Petersen and Mark J. van der Laan
Am. J. Epidemiol. 2007 166: 1003-1004.[Extract] [FREE Full Text] - Invited Commentary: Effect Modification by Time-varying Covariates
- James M. Robins, Miguel A. Hernán, and Andrea Rotnitzky
Am. J. Epidemiol. 2007 166: 994-1002.[Abstract] [FREE Full Text]
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 J. M. Robins, M. A. Hernan, and A. Rotnitzky Invited Commentary: Effect Modification by Time-varying Covariates Am. J. Epidemiol., November 1, 2007; 166(9): 994 - 1002. [Abstract] [Full Text] [PDF]
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M. L. Petersen and M. J. van der Laan Petersen et al. Respond to "Effect Modification by Time-varying Covariates" Am. J. Epidemiol., November 1, 2007; 166(9): 1003 - 1004. [Full Text] [PDF]
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