Quantifying ethical tradeoffs for vaccine efficacy trials during severe epidemics
Bibliographic record
Abstract
Abstract Background During emerging epidemics of highly fatal diseases, rapid development and testing of new vaccines may be critical to curbing transmission and saving lives. However, the design of vaccine efficacy trials in such contexts may face considerable logistical, epidemiological, or ethical impediments. Three different vaccine efficacy trials were conducted during the 2014-2016 Ebola virus epidemic in West Africa, each with different designs. At the time, there was vigorous debate on the tradeoff between a trial’s ability to yield information of scientific and societal value versus the perceived ethical dilemma of withholding potentially life-saving vaccines from control participants. Whereas the scientific value of a trial is often estimated in terms of statistical power, speed, and rigor, we lack similar metrics for the ethical costs of withholding interventions. Methods and Findings Here, we introduce a conceptual framework that fills this gap and allows quantitative assessment of both the scientific value of a study and the risks incurred by trial participants. We show that even untested vaccines against severe diseases may be probabilistically beneficial—i.e. after accounting for realistic uncertainty in their safety and efficacy, trial participants are expected to be better off vaccinated than not. While accounting for this uncertainty, we estimate trial participant risk under a hypothetical, idealized vaccine rollout scenario and compare it to risk under various candidate trial designs, in order to elucidate specific quantitative tradeoffs between cumulative risk to trial participants and information gained. Through an illustrative simulation example, we highlight specific trial-design modifications that allow for conscientious balance between minimizing participant risk and acquiring information of societal value. These include modifications that affect the speed with which a trial would detect an efficacious vaccine (greater sample size or enrollment rate, interim analyses, or risk-prioritized vaccine rollout), which leads to earlier vaccination of control participants should the vaccine be efficacious, and those that systematically limit the risk “spent” by unvaccinated individuals (e.g., providing vaccination to controls after a delay, or presumptive vaccination of subjects above a risk threshold). Conclusion We advocate this conceptual approach as a means of clarifying the tensions between opposing viewpoints and facilitating transparent discussion to aid ethical and efficient responses to future emerging epidemics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.234 | 0.514 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".