Trial Forge Guidance 5: ethical considerations in randomised Studies Within A Trial (SWATs)
Bibliographic record
Abstract
BACKGROUND: Randomised trials often struggle with recruitment, retention, and delays, impacting both finances and patient care. To improve trial processes, trialists can do Studies Within A Trial (SWATs) that compare trial process alternatives. A SWAT is a self-contained research study that is embedded within a host trial, or several host trials, with the aim of evaluating or exploring alternative ways of delivering or organising a particular trial process. Although SWATs are recognised by funders, there are differences in how they are implemented, such as different consent requirements in the UK and Ireland. This complicates SWAT conduct, raises ethical considerations, and highlights the need for standardised, ethical approaches to SWATs. The purpose of the current study was to devise guidance to address this. METHODS: We used existing systematic reviews, searched PubMed and the SWAT register, and contacted SWAT teams known to the authors to identify relevant randomised SWATs to include in our literature review. We extracted information on SWAT descriptives and 19 outcomes of interest pre-identified by the authors as being potential ethical considerations. We themed our findings. We held three consensus building fora, all including representatives from eight key stakeholder groups representing a broad range of roles in trials. We presented participants with two SWATs to start conversation on perceived ethical issues. We also incorporated the findings of our literature review. Consensus building fora were recorded, transcribed, and analysed using NVivo, focusing on ethical principles, challenges, and solutions. RESULTS: We developed guidance on ethical considerations applicable to randomised SWATs. There are 14 ethical considerations covering all stages of a SWAT, from development and SWAT team selection to communication of results. The considerations are posed as questions, so trial teams can easily answer these when designing their SWAT. CONCLUSIONS: Studies Within A Trial are generally perceived to be low risk and low burden to participants. However, there are still varying ethical standards applied to these studies by researchers, sponsors, and ethics committees. These guidelines will be helpful to anyone planning or reviewing SWATs in understanding these differences and their ethical implications, and provide a practical guide for the ethical conduct of SWATs.
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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 | 0.758 | 0.860 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.009 | 0.048 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.013 | 0.019 |
| Research integrity | 0.066 | 0.041 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".