Original Contribution 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 usingobservational data, particularlywhen time-dependent confounding is present. In recent statisticalwork, vander 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 effectmodification 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 changeover time. This article provides apractical 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 fromSanFrancisco,California, collectedbetween2000 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 Proteas