Review of an innovative approach to practical trials: the ‘cohort multiple RCT’ design
Bibliographic record
Abstract
The ‘cohort multiple randomised controlled trial’ (cmRCT) is an innovative approach to the design and conduct of RCTs which compare the effectiveness of interventions to usual care (Relton et al, 2010). The design utilises a large long term observational cohort of people with the condition of interest, regularly measuring the outcomes of the whole cohort. The cohort in the cmRCT design allows multiple trial populations to be quickly identified and recruited and interventions tested against usual care. Information consent processes are similar to those in routine healthcare. Studies using the design were identified through citations of the original theoretical article (Relton et al 2010). Data were extracted from published articles, study protocols and presentations. 16 studies implementing the cmRCT design were identified with a total of 18 ongoing or completed trials were embedded within these cohorts. Some cohorts focussed on a single disease or injury (e.g. hip fracture, breast cancer, colorectal cancer), others had a wider focus (e.g. risk of mental health conditions, risk of falls). Some studies built a cohort around a trial, and then obtained further funds to exploit the cohort for further trials within that cohort. This review of the cmRCT design in practice provides examples of the design in the UK, Canada and the Netherlands and will help guide researchers interested in using the cmRCT design. Future research needs to assess the acceptability and efficiency of this approach, i.e., if/when this design is preferable to the standard approach to single separate RCTs.
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 | 0.310 | 0.659 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".