Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

The evolving HIV prevention landscape poses challenges to the statistical design of future trials of candidate HIV vaccines. Study designs must address the anticipated reduction in HIV incidence due to adding new prevention modalities to the standard prevention package provided to trial participants, and must also accommodate individual choices of participants with regard to the use of these modalities. We explore four potential trial designs that address these challenges, with a focus on accommodating the newest addition to the prevention package-antiretroviral-based oral pre-exposure prophylaxis (PrEP). The designs differ with respect to how individuals who take up oral PrEP at screening are handled. An All-Comers Design enrolls and randomizes all eligible individuals, a Decliners Design enrolls and randomizes only those who decline PrEP at screening, and Single and Multi-Stage Run-In Designs enroll all but randomize only those who decline PrEP or show inadequate adherence to PrEP after one or multiple run-in periods. We compare these designs with respect to required sample sizes, study duration, and resource requirements, using a simulation model that incorporates data on HIV risk and PrEP uptake and adherence among men who have sex with men (MSM) in the Americas. We advocate considering Run-In Designs for some future contexts, and identify their advantages and tradeoffs relative to the other designs. The design concepts apply beyond HIV vaccines to other prevention modalities being developed with the aim to achieve further reductions in HIV incidence.

S ummary: Given recent advances in HIV prevention, future trials of many experimental interventions are likely to be “active-controlled” designs, whereby HIV negative individuals are randomized to the experimental intervention or an active control known to be effective based on a historical trial. The efficacy of the experimental intervention to prevent HIV infection relative to placebo cannot be evaluated directly based on the trial data alone. One approach that has been proposed is to leverage an HIV exposure marker, such as incident rectal gonorrhea which is highly correlated with HIV infection in populations of men who have sex with men (MSM). Assuming we can fit a model associating HIV incidence and incidence of the exposure marker, based on data from multiple historical studies, incidence of the marker in the active-controlled trial population can be used to infer the HIV incidence that would have been observed had a placebo arm been included, i.e. a “counterfactual placebo”, and to evaluate efficacy of the experimental intervention relative to this counterfactual placebo. We formalize this approach and articulate the underlying assumptions, develop an estimation approach and evaluate its performance in finite samples, and discuss the implications of our findings for future development and application of the approach in HIV prevention. Improved HIV exposure markers and careful assessment of assumptions and study of their violation are needed before the approach is applied in practice.

While rapid development and roll out of COVID-19 vaccines is necessary in a pandemic, the process limits the ability of clinical trials to assess longer-term vaccine efficacy. We leveraged COVID-19 surveillance data in the U.S. to evaluate vaccine efficacy in U.S. Government-funded COVID-19 vaccine efficacy trials with a three-step estimation process. First, we used a compartmental epidemiological model informed by county-level surveillance data, a "population model", to estimate SARS-CoV-2 incidence among the unvaccinated. Second, a "cohort model" was used to adjust the population SARS-CoV-2 incidence to the vaccine trial cohort, taking into account individual participant characteristics and the difference between SARS-CoV-2 infection and COVID-19 disease. Third, we fit a regression model estimating the offset between the cohort-model-based COVID-19 incidence in the unvaccinated with the placebo-group COVID-19 incidence in the trial during blinded follow-up. Counterfactual placebo COVID-19 incidence was estimated during open-label follow-up by adjusting the cohort-model-based incidence rate by the estimated offset. Vaccine efficacy during open-label follow-up was estimated by contrasting the vaccine group COVID-19 incidence with the counterfactual placebo COVID-19 incidence. We documented good performance of the methodology in a simulation study. We also applied the methodology to estimate vaccine efficacy for the two-dose AZD1222 COVID-19 vaccine using data from the phase 3 U.S. trial (ClinicalTrials.gov # NCT04516746). We estimated AZD1222 vaccine efficacy of 59.1% (95% uncertainty interval (UI): 40.4%-74.3%) in April, 2021 (mean 106 days post-second dose), which reduced to 35.7% (95% UI: 15.0%-51.7%) in July, 2021 (mean 198 days post-second-dose). We developed and evaluated a methodology for estimating longer-term vaccine efficacy. This methodology could be applied to estimating counterfactual placebo incidence for future placebo-controlled vaccine efficacy trials of emerging pathogens with early termination of blinded follow-up, to active-controlled or uncontrolled COVID-19 vaccine efficacy trials, and to other clinical endpoints influenced by vaccination.