Evidence for risk of bias in cluster randomised trials: review of recent trials published in three general medical journals
Bibliographic record
Abstract
OBJECTIVE: To examine the prevalence of a risk of bias associated with the design and conduct of cluster randomised controlled trials among a sample of recently published studies. DESIGN: Retrospective review of cluster randomised trials published in the BMJ, Lancet, and New England Journal of Medicine from January 1997 to October 2002. MAIN OUTCOME MEASURES: Prevalence of secure randomisation of clusters, identification of participants before randomisation (to avoid foreknowledge of allocation), differential recruitment between treatment arms, differential application of inclusion and exclusion criteria, and differential attrition. RESULTS: Of the 36 trials identified, 24 were published in the BMJ,11 in the Lancet, and a single trial in the New England Journal of Medicine. At the cluster level, 15 (42%) trials provided evidence for secure allocation and 25 (69%) used stratified allocation. Few trials showed evidence of imbalance at the cluster level. However, some evidence of susceptibility to risk of bias at the individual level existed in 14 (39%) studies. CONCLUSIONS: Some recently published cluster randomised trials may not have taken adequate precautions to guard against threats to the internal validity of their design.
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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 | 0.173 | 0.546 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.016 |
| Bibliometrics | 0.025 | 0.024 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".