Marginal structural models for estimating the effect of highly active antiretroviral therapy initiation on CD4 cell count

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

Research output: Contribution to journalArticlepeer-review

89 Scopus citations


The effect of highly active antiretroviral therapy (HAART) on the evolution of CD4-positive T-lymphocyte (CD4 cell) count among human immunodeficiency virus (HIV)-positive participants was estimated using inverse probability-of-treatment-and-censoring (IPTC)-weighted estimation of a marginal structural model. Of 1,763 eligible participants from two US cohort studies followed between 1996 and 2002, 60 percent initiated HAART. The IPTC-weighted estimate of the difference in mean CD4 cell count at 1 year among participants continuously treated versus those never treated was 71 cells/mm3 (95% confidence interval: 47.5, 94.6), which agrees with the reported results of randomized experiments. The corresponding estimate from a standard generalized estimating equations regression model that included baseline and most recent CD4 cell count and HIV type 1 RNA viral load as regressors was 26 cells/mm 3 (95% confidence interval: 17.7, 34.3). These results indicate that nonrandomized studies of HIV treatment need to be analyzed with methods (e.g., IPTC-weighted estimation) that, in contrast to standard methods, appropriately adjust for time-varying covariates that are simultaneously confounders and intermediate variables. The 1-year estimate of 71 cells/mm3 was followed by an estimated continued increase of 29 cells/mm3 per year (estimated effect at 6 years: 216 cells/mm3), providing evidence that the large short-term effect found in randomized experiments persists and continues to improve over 6 years.

Original languageEnglish (US)
Pages (from-to)471-478
Number of pages8
JournalAmerican journal of epidemiology
Issue number5
StatePublished - Sep 2005


  • Acquired immunodeficiency syndrome
  • Antiretroviral therapy
  • Bias (epidemiology)
  • CD4 lymphocyte count
  • Causality
  • Confounding factors (epidemiology)
  • HIV
  • Highly active

ASJC Scopus subject areas

  • Epidemiology


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