Comparison of dynamic treatment regimes via inverse probability weighting

Basic Clin Pharmacol Toxicol. 2006 Mar;98(3):237-42. doi: 10.1111/j.1742-7843.2006.pto_329.x.


Appropriate analysis of observational data is our best chance to obtain answers to many questions that involve dynamic treatment regimes. This paper describes a simple method to compare dynamic treatment regimes by artificially censoring subjects and then using inverse probability weighting (IPW) to adjust for any selection bias introduced by the artificial censoring. The basic strategy can be summarized in four steps: 1) define two regimes of interest, 2) artificially censor individuals when they stop following one of the regimes of interest, 3) estimate inverse probability weights to adjust for the potential selection bias introduced by censoring in the previous step, 4) compare the survival of the uncensored individuals under each regime of interest by fitting an inverse probability weighted Cox proportional hazards model with the dichotomous regime indicator and the baseline confounders as covariates. In the absence of model misspecification, the method is valid provided data are available on all time-varying and baseline joint predictors of survival and regime discontinuation. We present an application of the method to compare the AIDS-free survival under two dynamic treatment regimes in a large prospective study of HIV-infected patients. The paper concludes by discussing the relative advantages and disadvantages of censoring/IPW versus g-estimation of nested structural models to compare dynamic regimes.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural
  • Review

MeSH terms

  • Acquired Immunodeficiency Syndrome / drug therapy
  • Acquired Immunodeficiency Syndrome / epidemiology*
  • Anti-HIV Agents / administration & dosage
  • Anti-HIV Agents / therapeutic use*
  • Antiretroviral Therapy, Highly Active*
  • Clinical Trials as Topic
  • Data Interpretation, Statistical
  • Disease-Free Survival
  • Drug Administration Schedule
  • Humans
  • Models, Statistical
  • Pharmacoepidemiology* / methods
  • Probability*
  • Selection Bias
  • Treatment Refusal


  • Anti-HIV Agents