Using a network-based approach and targeted maximum likelihood estimation to evaluate the effect of adding pre-exposure prophylaxis to an ongoing test-and-treat trial

Abstract

Background: Several cluster-randomized trials are underway to investigate the implementation and effectiveness of a universal test-and-treat strategy on the HIV epidemic in sub-Saharan Africa. We consider nesting studies of pre-exposure prophylaxis within these trials. Pre-exposure prophylaxis is a general strategy where high-risk HIV- persons take antiretrovirals daily to reduce their risk of infection from exposure to HIV. We address how to target pre-exposure prophylaxis to high-risk groups and how to maximize power to detect the individual and combined effects of universal test-and-treat and pre-exposure prophylaxis strategies.

Methods: We simulated 1000 trials, each consisting of 32 villages with 200 individuals per village. At baseline, we randomized the universal test-and-treat strategy. Then, after 3 years of follow-up, we considered four strategies for targeting pre-exposure prophylaxis: (1) all HIV- individuals who self-identify as high risk, (2) all HIV- individuals who are identified by their HIV+ partner (serodiscordant couples), (3) highly connected HIV- individuals, and (4) the HIV- contacts of a newly diagnosed HIV+ individual (a ring-based strategy). We explored two possible trial designs, and all villages were followed for a total of 7 years. For each village in a trial, we used a stochastic block model to generate bipartite (male-female) networks and simulated an agent-based epidemic process on these networks. We estimated the individual and combined intervention effects with a novel targeted maximum likelihood estimator, which used cross-validation to data-adaptively select from a pre-specified library the candidate estimator that maximized the efficiency of the analysis.

Results: The universal test-and-treat strategy reduced the 3-year cumulative HIV incidence by 4.0% on average. The impact of each pre-exposure prophylaxis strategy on the 4-year cumulative HIV incidence varied by the coverage of the universal test-and-treat strategy with lower coverage resulting in a larger impact of pre-exposure prophylaxis. Offering pre-exposure prophylaxis to serodiscordant couples resulted in the largest reductions in HIV incidence (2% reduction), and the ring-based strategy had little impact (0% reduction). The joint effect was larger than either individual effect with reductions in the 7-year incidence ranging from 4.5% to 8.8%. Targeted maximum likelihood estimation, data-adaptively adjusting for baseline covariates, substantially improved power over the unadjusted analysis, while maintaining nominal confidence interval coverage.

Conclusion: Our simulation study suggests that nesting a pre-exposure prophylaxis study within an ongoing trial can lead to combined intervention effects greater than those of universal test-and-treat alone and can provide information about the efficacy of pre-exposure prophylaxis in the presence of high coverage of treatment for HIV+ persons.

Keywords: Adaptive pre-specification; HIV; cluster-randomized trials; networks; pre-exposure prophylaxis; targeted maximum likelihood estimation.

Conflict of interest statement

Declaration of conflicting interests

The authors declare no conflict of interest.

Figures

Figure 1

Proposed Design 1: The universal test-and-treat (UTT) assignment (high vs. lower coverage) is randomized with balanced allocation to the 32 villages. After three years of follow-up, the PrEP assignment (high vs. no coverage) is randomized within the UTT intervention arm (high coverage). All villages are followed for four additional years after the second randomization.

Figure 2

Proposed Design 2: The universal test-and-treat (UTT) assignment (high vs. lower coverage) is randomized with balanced allocation to the 32 villages. After three years of follow-up, the coverage of antiretroviral therapy is scaled-up in the UTT control arm (from low to high coverage), and the PrEP assignment (high vs. no coverage) is randomized within both UTT arms separately. All villages are followed for four additional years after the second randomization.

Figure 3

Mixing diagram (left) and the block matrix (right) for our bipartite, degree-corrected, stochastic block model. On the mixing diagram, line thickness represents the proportion of edges (connections) between each block. For the block matrix, F1 – F4 represent the four female blocks, and M1 – M4 represent the four male blocks, and the shading represents the propensity to mix (form connections) within or across blocks.