An adaptive design for case-driven vaccine efficacy study when incidence rate is unknown
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In many vaccine efficacy studies where the endpoint is a rare infection/disease event, an event-driven design is commonly used for testing the hypothesis that study vaccine lowers the risk of the event. Uncertainty of the incidence rate has a large impact on the sample size and study duration. To mitigate the risk of running a potentially large, long-duration efficacy trial with an uncertain event rate, we propose a two-stage adaptive design strategy with interim analyses to allow evaluation of study feasibility and sample size adaptation. During Stage I, a modest number of subjects will be enrolled and the feasibility of the study will be evaluated based on the incidence rate observed. If the feasibility of the study is established, at the end of Stage I a formal interim analysis will be performed, with a potential sample size adaptation based on the conditional rejection probability approach. The operating characteristics of this design are evaluated via simulation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it