Structural accelerated failure time models for survival analysis in studies with time-varying treatments

Miguel A. Hernán, Stephen R. Cole, Joseph Margolick, Mardge Cohen, James M. Robins

Research output: Contribution to journalArticlepeer-review

Abstract

Background: In the absence of unmeasured confounding and model misspecification, standard methods for estimating the causal effect of time-varying treatments on survival are biased when (i) there exists a time-dependent risk factor for survival that also predicts subsequent treatment and (ii) past treatment history predicts subsequent risk factor level. In contrast, structural models provide consistent estimates of causal effects when unmeasured confounding and model misspecification are absent. The parameters of nested structural models are estimated by g-estimation and those of marginal structural models by inverse probability weighting. Methods: We describe a nested structural accelerated failure time model and use it to estimate the total causal effect of highly active antiretroviral therapy (HAART) on the time to AIDS or death among human immunodeficiency virus (HIV)-infected participants of the Multicenter AIDS Cohort and Women's Interagency HIV Studies. The Appendix describes g-estimation and methods to deal with censoring. Results: Comparing the regime 'always treated' to 'never treated,' the AIIDS-free survival time ratio was 2.5 (95% confidence interval [CI]: 1.7, 3.3). Conclusions: Our finding of a strongly beneficial effect is consistent with results from randomized trials and from a previous analysis of the same data using a marginal structural Cox model. In contrast, a previous analysis using a standard (non-structural) model did not find an effect of treatment on survival.

Original languageEnglish (US)
Pages (from-to)477-491
Number of pages15
JournalPharmacoepidemiology and Drug Safety
Volume14
Issue number7
DOIs
StatePublished - Jul 2005

Keywords

  • Causal inference
  • Cohort studies
  • Confounding

ASJC Scopus subject areas

  • Epidemiology
  • Pharmacology (medical)

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