History-adjusted marginal structural models for estimating time-varying effect modification.
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 non suppressive regimen and how this effect differs depending on CD4-positive T-lymphocyte count.
Petersen
Maya L
ML
Division of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA 94720, USA. mayaliv@gmail.com
Deeks
Steven G
SG
Martin
Jeffrey N
JN
van der Laan
Mark J
MJ
D000465:Algorithms; D023241:Antiretroviral Therapy, Highly Active / Q000379:methods*; D015496:CD4-Positive T-Lymphocytes / Q000187:drug effects; D018414:CD8-Positive T-Lymphocytes / Q000187:drug effects; D015331:Cohort Studies; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D015658:HIV Infections / Q000188:drug therapy* / Q000453:epidemiology / Q000276:immunology; D006801:Humans; D008137:Longitudinal Studies; D018655:Lymphocyte Count; D008432:Mathematical Computing; D008954:Models, Biological; D015233:Models, Statistical; D016017:Odds Ratio; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States / Q000453:epidemiology