The effect of heterogeneity on HIV prevention trials

Recruitment of Female Sex Workers in HIV Prevention Trials: Can Efficacy Endpoints Be Reached More Efficiently?

BACKGROUND/SETTING

Randomized controlled trials (RCTs) of HIV biomedical prevention interventions often enroll participants with varying levels of HIV exposure, including people never exposed to HIV. We assessed whether enrolling larger proportion of participants with consistently high exposure to HIV, such as female sex workers (FSWs), might reduce trial duration and improve the accuracy of product efficacy estimates in future HIV prevention trials.

METHODS

We used an individual-based stochastic model to simulate event-driven RCTs of an HIV prevention intervention providing 80% reduction in susceptibility per act under different proportions of FSW enrolled. A 5% annual dropout rate was assumed for both FSW and non-FSW in our main scenario, but rates of up to 50% for FSW were also explored.

RESULTS

Enrolling 20% and 50% FSW reduced the median-simulated trial duration from 30 months with 0% FSW enrolled to 22 months and 17 months, respectively. Estimated efficacy increased from 71% for RCTs without FSW to 74% and 76% for RCTs with 20% and 50% FSW enrolled, respectively. Increasing the FSW dropout rate to 50% increased the duration of RCTs by 1-2 months on average and preserved the gain in estimated efficacy.

CONCLUSIONS

Despite the potential logistical challenges of recruiting and retaining FSW, trialists should revisit the idea of enrolling FSW in settings where HIV incidence among FSW is higher than among non-FSW. Our analysis suggests that enrolling FSW would increase HIV incidence, reduce trial duration, and improve efficacy estimates, even if the annual dropout rate among FSW participants is high.

Temporally Varying Relative Risks for Infectious Diseases: Implications for Infectious Disease Control

Risks for disease in some population groups relative to others (relative risks) are usually considered to be consistent over time, although they are often modified by other, nontemporal factors. For infectious diseases, in which overall incidence often varies substantially over time, the patterns of temporal changes in relative risks can inform our understanding of basic epidemiologic questions. For example, recent studies suggest that temporal changes in relative risks of infection over the course of an epidemic cycle can both be used to identify population groups that drive infectious disease outbreaks, and help elucidate differences in the effect of vaccination against infection (that is relevant to transmission control) compared with its effect against disease episodes (that reflects individual protection). Patterns of change in the age groups affected over the course of seasonal outbreaks can provide clues to the types of pathogens that could be responsible for diseases for which an infectious cause is suspected. Changing apparent efficacy of vaccines during trials may provide clues to the vaccine's mode of action and/or indicate risk heterogeneity in the trial population. Declining importance of unusual behavioral risk factors may be a signal of increased local transmission of an infection. We review these developments and the related public health implications.

HIV prevention trial design in an era of effective pre-exposure prophylaxis

Pre-exposure prophylaxis (PrEP) has demonstrated remarkable effectiveness protecting at-risk individuals from HIV-1 infection. Despite this record of effectiveness, concerns persist about the diminished protective effect observed in women compared with men and the influence of adherence and risk behaviors on effectiveness in targeted subpopulations. Furthermore, the high prophylactic efficacy of the first PrEP agent, tenofovir disoproxil fumarate/emtricitabine (TDF/FTC), presents challenges for demonstrating the efficacy of new candidates. Trials of new agents would typically require use of non-inferiority (NI) designs in which acceptable efficacy for an experimental agent is determined using pre-defined margins based on the efficacy of the proven active comparator (i.e. TDF/FTC) in placebo-controlled trials. Setting NI margins is a critical step in designing registrational studies. Under- or over-estimation of the margin can call into question the utility of the study in the registration package. The dependence on previous placebo-controlled trials introduces the same issues as external/historical controls. These issues will need to be addressed using trial design features such as re-estimated NI margins, enrichment strategies, run-in periods, crossover between study arms, and adaptive re-estimation of sample sizes. These measures and other innovations can help to ensure that new PrEP agents are made available to the public using stringent standards of evidence.

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

Inconsistent results in recent HIV prevention trials of pre-exposure prophylactic interventions may be due to heterogeneity in risk among study participants. Intervention effectiveness is most commonly estimated with the Cox model, which compares event times between populations. When heterogeneity is present, this population-level measure underestimates intervention effectiveness for individuals who are at risk. We propose a likelihood-based Bayesian hierarchical model that estimates the individual-level effectiveness of candidate interventions by accounting for heterogeneity in risk with a compound Poisson-distributed frailty term. This model reflects the mechanisms of HIV risk and allows that some participants are not exposed to HIV and, therefore, have no risk of seroconversion during the study. We assess model performance via simulation and apply the model to data from an HIV prevention trial. Copyright © 2016 John Wiley & Sons, Ltd.

Influence of trial duration on the bias of the estimated treatment effect in clinical trials when individual heterogeneity is ignored

In randomized clinical trials where the times to event of two treatment groups are compared under a proportional hazards assumption, it has been established that omitting prognostic factors from the model entails an underestimation of the hazards ratio. Heterogeneity due to unobserved covariates in cancer patient populations is a concern since genomic investigations have revealed molecular and clinical heterogeneity in these populations. In HIV prevention trials, heterogeneity is unavoidable and has been shown to decrease the treatment effect over time. This article assesses the influence of trial duration on the bias of the estimated hazards ratio resulting from omitting covariates from the Cox analysis. The true model is defined by including an unobserved random frailty term in the individual hazard that reflects the omitted covariate. Three frailty distributions are investigated: gamma, log-normal, and binary, and the asymptotic bias of the hazards ratio estimator is calculated. We show that the attenuation of the treatment effect resulting from unobserved heterogeneity strongly increases with trial duration, especially for continuous frailties that are likely to reflect omitted covariates, as they are often encountered in practice. The possibility of interpreting the long-term decrease in treatment effects as a bias induced by heterogeneity and trial duration is illustrated by a trial in oncology where adjuvant chemotherapy in stage 1B NSCLC was investigated.

High incidence is not high exposure: what proportion of prevention trial participants are exposed to HIV?

Objective

Randomized clinical trials of HIV prevention in high-risk populations of women often assume that all participants have similar exposure to HIV. However, a substantial fraction of women enrolled in the trial may have no or low exposure to HIV. Our objective was to estimate the proportion of women exposed to HIV throughout a hypothetical high-risk study population.

Methods

A stochastic individual-based model was developed to simulate the sexual behavior and the risk of HIV acquisition for a cohort of sexually active HIV-uninfected women in high HIV prevalence settings. Key behavior and epidemic assumptions in the model were based on published studies on HIV transmission in South Africa. The prevalence of exposure, defined as the proportion of women who have sex with HIV-infected partner, and HIV incidence were evaluated.

Results

Our model projects that in communities with HIV incidence rate of 1 per 100 person years, only 5-6% of women are exposed to HIV annually while in communities with an HIV incidence of 5 per 100 person years 20-25% of women are exposed to HIV. Approximately 70% of the new infections are acquired from partners with asymptomatic HIV.

Conclusions

Mathematical models suggest that a high proportion of women enrolled in HIV prevention trials may be unexposed to HIV even when incidence rates are high. The relationship between HIV exposure and other risk factors should be carefully analyzed when future clinical trials are planned.

Estimating the per-exposure effect of infectious disease interventions

The average effect of an infectious disease intervention (eg, a vaccine) varies across populations with different degrees of exposure to the pathogen. As a result, many investigators favor a per-exposure effect measure that is considered independent of the population level of exposure and that can be used in simulations to estimate the total disease burden averted by an intervention across different populations. However, while per-exposure effects are frequently estimated, the quantity of interest is often poorly defined, and assumptions in its calculation are typically left implicit. In this article, we build upon work by Halloran and Struchiner (Epidemiology. 1995;6:142-151) to develop a formal definition of the per-exposure effect and discuss conditions necessary for its unbiased estimation. With greater care paid to the parameterization of transmission models, their results can be better understood and can thereby be of greater value to decision-makers.

Estimating the effectiveness in HIV prevention trials by incorporating the exposure process: application to HPTN 035 data

Estimating the effectiveness of a new intervention is usually the primary objective for HIV prevention trials. The Cox proportional hazard model is mainly used to estimate effectiveness by assuming that participants share the same risk under the covariates and the risk is always non-zero. In fact, the risk is only non-zero when an exposure event occurs, and participants can have a varying risk to transmit due to varying patterns of exposure events. Therefore, we propose a novel estimate of effectiveness adjusted for the heterogeneity in the magnitude of exposure among the study population, using a latent Poisson process model for the exposure path of each participant. Moreover, our model considers the scenario in which a proportion of participants never experience an exposure event and adopts a zero-inflated distribution for the rate of the exposure process. We employ a Bayesian estimation approach to estimate the exposure-adjusted effectiveness eliciting the priors from the historical information. Simulation studies are carried out to validate the approach and explore the properties of the estimates. An application example is presented from an HIV prevention trial.

Coverage, context and targeted prevention: optimising our impact

Development of efficacious interventions is only the first step in achieving population level impact. Efficacious interventions impact infection levels in the population only if they are implemented at the right scale. Coverage must be prioritised across subpopulations based on the diversity and clustering of infections and risk in society, and expanded rapidly without delay. It is important to prioritise those who are most likely to transmit infection first.