Guiding Vaccine Efficacy Trial Design During Public Health Emergencies: An interactive web-based decision support tool
Bibliographic record
Abstract
Abstract The design and execution of rigorous, fast, and ethical vaccine efficacy trials can be challenging during epidemics of emerging pathogens, such as the 2014-2016 Ebola virus and 2015-2016 Zika virus epidemics. Response to an urgent public health crisis requires accelerated research even as emerging epidemics themselves change rapidly and are inherently less well understood than well-established diseases. As part of the World Health Organization Research and Development Blueprint, we designed a web-based interactive decision support system (InterVax-Tool) to help diverse stakeholders navigate the epidemiological, logistical, and ethical decisions involved in designing a vaccine efficacy trial during a public health emergency. In contrast to existing literature on trial design, InterVax-Tool offers high-level visual and interactive assistance through a set of four decision trees, guiding users through selection of 1) the Primary Endpoint, (2) the Target Population, (3) Randomization, and (4) the Comparator. Guidance is provided on how each of fourteen key considerations–grouped as Epidemiological, Vaccine-related, Infrastructural, or Sociocultural–should be used to inform each decision in the trial design process. The tool is not intended to provide a black box decision framework for identifying an optimal trial design, but rather to facilitate transparent, collaborative and comprehensive discussion of the relevant decisions, while recording the decision process. The tool can also assist capacity building by providing a cross-disciplinary picture of trial design using concepts from epidemiology, study design, vaccinology, biostatistics, mathematical modeling and clinical research ethics. Here, we describe the goals and features of InterVax-Tool as well as its application to the design of a Zika vaccine efficacy trial. One Sentence Summary An interactive web-based decision support tool was developed to assist in the design of vaccine efficacy trials during emerging outbreaks.
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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 | 0.049 | 0.123 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.009 |
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".