Estimating effectiveness in HIV prevention trials with a Bayesian hierarchical compound Poisson frailty model

Joint modeling of time-varying HIV exposure and infection for estimation of per-act efficacy in HIV prevention trials

Objectives

Using 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.

Methods

Within 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.

Results

We 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.

Conclusions

The proposed joint modeling, along with the proposed empirical Bayes estimation approach, can provide valid estimation of the per-exposure efficacy of a preventive intervention.

Adaptive non-inferiority margins under observable non-constancy

A central assumption in the design and conduct of non-inferiority trials is that the active-control therapy will have the same degree of effectiveness in the planned non-inferiority trial as in the prior placebo-controlled trials used to define the non-inferiority margin. This is referred to as the 'constancy' assumption. If the constancy assumption fails, decisions based on the chosen non-inferiority margin may be incorrect, and the study runs the risk of approving an inferior product or failing to approve a beneficial product. The constancy assumption cannot be validated in a trial without a placebo arm, and it is unlikely ever to be met completely. When there are strong, observable predictors of constancy, such as dosing and adherence to the active-control product, we can specify conditions where the constancy assumption will likely fail. We propose a method for using measurable predictors of active-control effectiveness to specify non-inferiority margins targeted to the planned study population characteristics. We describe a pre-specified method, using baseline characteristics or post-baseline predictors in the active-control arm, to adapt the non-inferiority margin at the end of the study if constancy is violated. Adaptive margins can help adjust for constancy violations that will inevitably occur in real clinical trials, while maintaining pre-specified levels of Type I error and power.