Beating the Placebo in HIV Prevention Efficacy Trials
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.416 | 0.604 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".