OBJECTIVES: To produce an effect on the HIV epidemic, preventive interventions need to achieve a minimum level of efficacy to offset potential indirect effects such as an increase in risky behavior. The current generation of HIV prevention trials on oral preexposure prophylaxis and on vaginal microbicides were designed using different set points for minimum individual-level efficacy (MIE). Some trials were designed not only to show superiority over placebo but also to rule out lower efficacies. The MIE has a substantial impact on the size and cost of a trial. Ideally, the MIE should be chosen to reduce uncertainty in the estimation of population-level effects. In this article, we investigate the effect of MIE on estimates of population-level impact to better inform trial design. METHODS: We used mathematical model simulations assuming various rates of efficacy obtained from trials and different MIEs to study the impact of wide-scale interventions on 2 public health indicators. RESULTS: Implementation factors were the main drivers of uncertainty in public health indicators for an intervention, although MIE also contributed. The level of uncertainty introduced by the MIE was substantially lower than that of the other factors. CONCLUSIONS: Investigators in clinical trials have set the MIE solely on the basis of potential public health impact. However, the substantial increase in trial costs associated with a large MIE is unlikely to be justified. These additional funds would be better spent in evaluating more critical implementation factors that cannot be assessed in clinical trials.
OBJECTIVES: To produce an effect on the HIV epidemic, preventive interventions need to achieve a minimum level of efficacy to offset potential indirect effects such as an increase in risky behavior. The current generation of HIV prevention trials on oral preexposure prophylaxis and on vaginal microbicides were designed using different set points for minimum individual-level efficacy (MIE). Some trials were designed not only to show superiority over placebo but also to rule out lower efficacies. The MIE has a substantial impact on the size and cost of a trial. Ideally, the MIE should be chosen to reduce uncertainty in the estimation of population-level effects. In this article, we investigate the effect of MIE on estimates of population-level impact to better inform trial design. METHODS: We used mathematical model simulations assuming various rates of efficacy obtained from trials and different MIEs to study the impact of wide-scale interventions on 2 public health indicators. RESULTS: Implementation factors were the main drivers of uncertainty in public health indicators for an intervention, although MIE also contributed. The level of uncertainty introduced by the MIE was substantially lower than that of the other factors. CONCLUSIONS: Investigators in clinical trials have set the MIE solely on the basis of potential public health impact. However, the substantial increase in trial costs associated with a large MIE is unlikely to be justified. These additional funds would be better spent in evaluating more critical implementation factors that cannot be assessed in clinical trials.
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