title: Effect modification by time-varying covariates.
abstract: Marginal structural models (MSMs) allow estimation of effect modification by baseline covariates, but they are less useful for estimating effect modification by evolving time-varying covariates. Rather, structural nested models (SNMs) were specifically designed to estimate effect modification by time-varying covariates. In their paper, Petersen et al. (Am J Epidemiol 2007;166:985-993) describe history-adjusted MSMs as a generalized form of MSM and argue that history-adjusted MSMs allow a researcher to easily estimate effect modification by time-varying covariates. However, history-adjusted MSMs can result in logically incompatible parameter estimates and hence in contradictory substantive conclusions. Here the authors propose a more restrictive definition of history-adjusted MSMs than the one provided by Petersen et al. and compare the advantages and disadvantages of using history-adjusted MSMs, as opposed to SNMs, to examine effect modification by time-dependent covariates.
authors: Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
authors: James M
authors: JM
authors: Robins
authors: Miguel A
authors: MA
authors: Hernán
authors: Andrea
authors: A
authors: Rotnitzky
mesh_terms: D023241:Antiretroviral Therapy, Highly Active / Q000379:methods*; D015331:Cohort Studies; D015986:Confounding Factors, Epidemiologic; D015658:HIV Infections / Q000188:drug therapy* / Q000453:epidemiology / Q000276:immunology; D006801:Humans; D016015:Logistic Models; D008137:Longitudinal Studies; D008432:Mathematical Computing; D015233:Models, Statistical; D016016:Proportional Hazards Models; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States / Q000453:epidemiology