Effect of highly active antiretroviral therapy on time to acquired immunodeficiency syndrome or death using marginal structural models.

Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA.
American Journal of Epidemiology (Impact Factor: 5.23). 11/2003; 158(7):687-94.
Source: PubMed


To estimate the net (i.e., overall) effect of highly active antiretroviral therapy (HAART) on time to acquired immunodeficiency syndrome (AIDS) or death, the authors used inverse probability-of-treatment weighted estimation of a marginal structural model, which can appropriately adjust for time-varying confounders affected by prior treatment or exposure. Human immunodeficiency virus (HIV)-positive men and women (n = 1,498) were followed in two ongoing cohort studies between 1995 and 2002. Sixty-one percent (n = 918) of the participants initiated HAART during 6,763 person-years of follow-up, and 382 developed AIDS or died. Strong confounding by indication for HAART was apparent; the unadjusted hazard ratio for AIDS or death was 0.98. The hazard ratio from a standard time-dependent Cox model that included time-varying CD4 cell count, HIV RNA level, and other time-varying and fixed covariates as regressors was 0.81 (95% confidence interval: 0.61, 1.07). In contrast, the hazard ratio from a marginal structural survival model was 0.54 (robust 95% confidence interval: 0.38, 0.78), suggesting a clinically meaningful net benefit of HAART. Standard Cox analysis failed to detect a clear net benefit, because it does not appropriately adjust for time-dependent covariates, such as HIV RNA level and CD4 cell count, that are simultaneously confounders and intermediate variables.

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    • "Marginal structural modeling (MSM) can control for time-dependent confounders affected by prior treatment [54]. Under some conditions, the treatment estimate from a MSM can have the same causal interpretation as an estimate from a randomized clinical trial [55]. Only the Tentori et al. study reported detailed data regarding the survival advantage of patients treated with active vitamin D. The unadjusted baseline Cox model and time-varying MSM models demonstrated a 16% and 22%, respectively, reduction of all-cause mortality associated with active vitamin D treatment. "
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    BMC Nephrology 09/2013; 14(1):199. DOI:10.1186/1471-2369-14-199 · 1.69 Impact Factor
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    • "conceptually similar to methodology for addressing differential censoring in the context of propensity score weighting : inverse probability of censoring weights is multiplied by propensity score weights and this composite weight is used in the final analysis ( Cole et al . 2003 , 2010 ; Cain and Cole 2009 ) . It is also similar to the multiplication of survey sampling weights by nonresponse adjustment weights , as is commonly performed in survey analysis ( Groves et al . 2004 ) . EXAMPLE 1 : SIMULATION STUDY We first describe a simple simulation study used to assess the performance of propensity score methods "
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    • "In our models for || , , 0, , we summarized the dependence on by V and L t , the most recently available values of CD4 cell count (restricted cubic spline with five knots) and HIV-1 RNA (<10,000, 10,000-100,000, ≥100,000 copies/mL) at time t, and months between time t and the most recent laboratory measurement (0, 1-2, 3-4, 5-6, ≥7). Like in previous analyses of observational HIV data (Cole et al., 2003; Hernán et al., 2000; Hernán et al., 2002; Sterne et al., 2005) we assumed that treatment was never stopped once initiated. Therefore, for each individual, the factors in the denominator of the weights W t were set to 1 for times t subsequent to treatment initiation, and estimated from the data for all other times, i.e., times when 0. The conditional probability of treatment initiation was estimated by fitting the pooled logistic regression model logit Prrr 1| 0, 0, , ′ ′ where is a month-specific intercept (restricted cubic splines with four knots), ′ and ′ are the transposes of the column vectors of log hazard ratios for the components of the baseline covariates V and the time-varying covariates L t , respectively. "
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