Computing threshold antibody levels of protection in vaccine clinical trials: An assessment of methodological bias
Bibliographic record
Abstract
In the development of new vaccines, understanding the level of vaccine-induced antibody that is sufficient to protect against disease can simplify and expedite the development and licensing process. If there is an accepted threshold antibody level that is indicative of protection, then smaller trials measuring antibody concentration alone can be conducted to test new vaccines, instead of large efficacy studies powered on clinical outcomes. Commonly, threshold levels of protective antibody are determined from clinical efficacy trials in which clinical endpoints are measured on everyone and a small subset of participants have antibody concentrations measured. The proportion of participants with antibody below a threshold in each group in the immunogenicity subset can be compared to the proportions with disease in each group in the larger trial to find an appropriate threshold. Mathematically, this method seeks to compute an absolute threshold whereby antibody above the threshold provides complete, sterilizing immunity. However, in practice it is often understood that such thresholds may only be indicative of a relative degree of protection rather than an absolute one. Although this approach is common, the accuracy of such methods when the underlying mathematical assumptions do not hold true, has never been tested. We simulated data from clinical trial scenarios under varying assumptions of vaccine efficacy and calculated antibody thresholds of protection. We estimated the bias in the calculated thresholds derived from each scenario and showed that in many situations this method produces inflated estimates of thresholds, particularly if a vaccine induces high levels of antibody or when the underlying assumption of sterilizing immunity is violated.
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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 | 0.740 | 0.874 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".