ArticleStatistical communications in infectious diseases2020
Joint modeling of time-varying HIV exposure and infection for estimation of per-act efficacy in HIV prevention trials.
Article in Statistical communications in infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesUsing the MTN-020/ASPIRE HIV prevention trial as a motivating example, our objective is to construct a joint model for the HIV exposure process through vaginal intercourse and the time to HIV infection in a population of sexually active women. By modeling participants' HIV infection in terms of exposures, rather than time exposed, our aim is to obtain a valid estimate of the per-act efficacy of a preventive intervention.
methodsWithin the context of HIV prevention trials, in which the frequency of sex acts is self-reported periodically by the participants, we model the exposure process of the trial participants with a non-homogeneous Poisson process. This approach allows for variability in the rate of sexual contacts between participants as well as variability in the rate of sexual contacts over time. The time to HIV infection for each participant is modeled as the time to the exposure that results in HIV infection, based on the modeled sexual contact rate. We propose an empirical Bayes approach for estimation.
resultsWe report the results of a simulation study where we evaluate the performance of our proposed approachandcompareittothetraditionalapproachofestimatingtheoverallreductioninHIVincidenceusing a Proportional Hazards Cox model. The proposed approach is also illustrated with data from the MTN-020/ASPIRE trial.
conclusionsThe proposed joint modeling, along with the proposed empirical Bayes estimation approach, can provide valid estimation of the per-exposure efficacy of a preventive intervention.
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