Statistical documentation for multi-disease, multi-domain platform trials: our experience with the Staphylococcus aureus Network Adaptive Platform trial
Bibliographic record
Abstract
Platform trials have become widely adopted across multiple disease areas over recent years, however, guidelines for operationalising these trials have not kept pace. We outline a series of documents that summarise the statistical components, and implicit processes, of the Staphylococcus aureus Network Adaptive Platform (SNAP) trial to provide an informal template for other researchers and reviewers of platform trials. We briefly summarise the content and role of the core protocol, statistical appendix, domain-specific appendices, simulation report, statistical implementation guides, data safety and monitoring committee (DSMC) reports, and domain-specific statistical analysis plans and final reports, and a transparent governance structure that ensures separate blinded and unblinded statistical teams. In the absence of guidelines or checklists for platform trial statistical documents, we hope to provide useful guidance to others in terms of what has worked so far for the SNAP trial, stimulate discussion, and inform a future consensus.Trial registration NCT05137119 . Registered on 30 November 2021.
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 | 0.574 | 0.788 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.033 | 0.022 |
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".