Antiretroviral Therapy and Mortality in Rural South Africa: A Comparison of Causal Modeling Approaches

Abstract

Estimation of causal effects from observational data is a primary goal of epidemiology. The use of multiple methods with different assumptions relating to exchangeability improves causal inference by demonstrating robustness across assumptions. We estimate the effect of antiretroviral therapy (ART) on mortality in rural KwaZulu-Natal, South Africa from 2007-2011 using two methods with substantially different assumptions: the regression discontinuity design (RDD), and inverse probability weighting of marginal structural models (IPW). The RDD analysis took advantage of a CD4 count-based threshold for ART initiation (200 cells/μl). The two methods yielded consistent but non-identical results for the effect of immediate initiation of ART (RDD intention-to-treat hazards ratio (HR) 0.66, 95% CI 0.35 to 1.26; RDD HR 0.56, 95% CI 0.41 to 0.77; IPW HR 0.49, 95% CI 0.42 to 0.58). Although RDD and IPW estimates had distinct identifying assumptions, strengths, and limitations in terms of internal and external validity, results in this application were similar. The differences in modeling approaches and external validity of each method may explain the minor differences in effect estimates, but the consistency of the results lends support for causal inference of the effect of ART and mortality from these data.