Abstract

WHO guidelines call for universal antiretroviral treatment, and UNAIDS has set a global target to virally suppress most HIV-positive individuals. Accurate estimates of population-level coverage at each step of the HIV care cascade (testing, treatment, and viral suppression) are needed to assess the effectiveness of "test and treat" strategies implemented to achieve this goal. The data available to inform such estimates, however, are susceptible to informative missingness: the number of HIV-positive individuals in a population is unknown; individuals tested for HIV may not be representative of those whom a testing intervention fails to reach, and HIV-positive individuals with a viral load measured may not be representative of those for whom no viral load is obtained. We provide an in-depth description of the statistical methods (target parameters, assumptions, statistical estimands, and algorithms) used in an interim analysis of the intervention arm of the SEARCH Study (NCT01864603) to analyze progress towards the UNAIDS 90-90-90 target at study baseline and after one and two years. We describe the methods used to account for informative measurement in all analyses as well as for informative censoring in longitudinal analyses. We use targeted maximum likelihood estimation (TMLE) with Super Learning to generate semi-parametric efficient and double robust estimates of the care cascade among a open cohort of prevalent HIV-positive adults and among a closed cohort of baseline HIV-positive adults. TMLE is also used to evaluate predictors of poor outcomes.

Disciplines

Biostatistics | Epidemiology | Statistical Methodology | Statistical Theory

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