[Marginal structural models application to estimate the effects of antiretroviral therapy in 5 cohorts of HIV seroconverters]

Abstract

Objectives: Standard methods to evaluate population effectiveness of treatments in observational studies have important limitations to appropriately adjust for time-dependent confounders. In this paper, we describe a recently developed methodological approach, marginal structural models (MSM), and use it to estimate the effectiveness of highly active antiretroviral therapy (HAART) on AIDS or death incidence.

Subjects and methods: We analyzed all subjects followed after 1997 as part of the GEMES project (comprised by several cohorts of HIV seroconverters in Spain) and who had not used HAART before the start of follow-up. To estimate the effect of HAART on AIDS or death incidence, we estimated the parameters of a marginal structural Cox model by fitting an inverse probability weighted logistic regression model. The estimation of the weights was based on CD4 count, time since seroconversion, sex, age, transmission category and previous treatment.

Results: 917 eligible subjects were followed for an average of 3.4 years and we observed 139 events. 42.1% of the participants received HAART during the study. The estimated rate ratio was 1.01 (95% confidence interval [CI], 0.68-1.49) using a Cox model without covariates and 0.90 (95% CI, 0.61-1.32) using a Cox model with time-dependent covariates. The causal rate ratio estimated for MSM was 0.74, (95% CI, 0.49-1.12).

Conclusions: The beneficial effect of HAART estimated by the MSM, but largely missed by conventional methods, is consistent with the findings of previous randomized studies. The MSM appropriately adjusted for the time-dependent covariate CD4 count, which is both a time-varying confounder and is affected by prior treatment.