Using the adherence‐efficacy relationship of TDF/FTC to calculate counterfactual background HIV incidence: A Bayesian modeling approach

Music City Center 

More commonly, randomized clinical trials of new agents for HIV pre-exposure prophylaxis (PrEP) compare against standard of care regimens without a placebo group. In the absence of a placebo group, it becomes intractable to estimate the efficacy of these new PrEP regimens. To remedy this, we propose a Bayesian modeling approach to estimate the counterfactual background HIV incidence (bHIV) in the context of randomized, double-blind, double-dummy, noninferiority trials to compare the novel interventions with the standard of care daily oral regimen of TDF/FTC for prevention of HIV infection in at-risk populations, where tenofovir diphosphate levels in dried blood spots (DBS) are assessed using case-cohort sampling. We construct a Poisson-based likelihood, incorporating DBS drug level data from case-cohort samples and imputing expected time in adherence categories for participants not selected in the case-cohort based on individual-level characteristics. Our model utilizes priors based on the adherence-efficacy relationship for TDF/FTC to back-calculate the counterfactual bHIV and uses Markov Chain Monte Carlo to estimate the comparative efficacy of the PrEP agents against bHIV.