Evaluation of injury prevention interventions using the stepped wedge cluster randomised trial design: key considerations
Bibliographic record
Abstract
BACKGROUND: Injury prevention interventions are often implemented at the group level via communities, hospitals, schools, etc, making cluster randomisation a suitable approach to evaluation. The stepped-wedge cluster randomised trial (SW-CRT) design has become increasingly popular for evaluating interventions in real-world settings. METHOD: In this commentary, we describe the methodological characteristics of the SW-CRT design and highlight key threats to validity, relevant design and analytical issues, and scenarios in which the SW-CRT design might be a reasonable design choice. We illustrate these key points using a recently completed SW-CRT: the prehospital Canadian C-Spine trial. RESULTS: Seven potential biases associated with SW-CRTs, including: (1) secular trends, (2) confounding by external factors, (3) identification and recruitment bias, (4) contamination, (5) late and early transitioning, (6) risks of baseline imbalances due to small numbers of clusters and (7) statistical issues are discussed, along with potential mitigation strategies. CONCLUSION: The SW-CRT design offers a pragmatic approach to evaluating injury prevention interventions that may involve a staggered rollout across services or regions. The design allows an intervention to be rolled out to all participating sites and provides an opportunity to efficiently evaluate effectiveness. It is important, however, for researchers to consider the unique design and analytic issues associated with the SW-CRT design. Mitigating potential threats to validity when using the SW-CRT design helps ensure robust evaluation of injury prevention interventions.
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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 | 0.582 | 0.779 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".