Use of multiple period, cluster randomised, crossover trial designs for comparative effectiveness research
Bibliographic record
Abstract
Many treatments are adopted into clinical practice without a solid evidence base and might be used heterogeneously across settings. Rigorous randomised controlled trials are therefore needed to inform decisions about the comparative effectiveness of treatments in common use. The mainstay of comparative effectiveness research is pragmatic trial design, which emphasises broad eligibility criteria, simple logistics, routinely collected outcome data, and cost efficient designs. Although treatment differences at the individual level might be small, they can become important when aggregated across large populations. To detect these small differences, very large trials are often required. In multiple period, cluster randomised, crossover trials, the study design randomises clusters (eg, hospitals) to exposure to different interventions in a randomly determined order, and is an attractive design for comparative effectiveness research. The trial design can be highly statistically efficient, compared with other competing designs, and can have many logistical advantages. Several prominent examples of this trial design have been published recently, yet practical guidance is lacking on how best to design these trials to ensure that they provide robust evidence. Some considerations include how to determine the frequency and number of crossovers, the importance of a time balanced design, how to determine the required sample size, and how to analyse appropriately. The justification for using this design (over a design randomising on the level of individual patients) also raises ethical concerns when used to evaluate individual level interventions without the prospective informed consent of individual participants. In this article, we outline the key methodological and ethical requirements needed for the robust design of multiple period, cluster randomised, crossover trials.
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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 | 0.400 | 0.505 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".