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

Am J Epidemiol. 2005 Sep 1;162(5):471-8. doi: 10.1093/aje/kwi216. Epub 2005 Aug 2.

Abstract

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/mm3 (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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Acquired Immunodeficiency Syndrome / drug therapy*
  • Adult
  • Antiretroviral Therapy, Highly Active / statistics & numerical data*
  • Bias
  • CD4 Lymphocyte Count / statistics & numerical data*
  • Causality
  • Confounding Factors, Epidemiologic
  • Disease Progression
  • Female
  • Humans
  • Male
  • Models, Statistical*