Inadequate reporting of sample size calculations in cluster randomised trials: a review
Bibliographic record
Abstract
To assess the adequacy of reporting sample size calculations in published cluster randomised trials (CRTs) and to evaluate the accuracy and justifications behind the a priori estimates used. A review was conducted of 166 CRTs reporting sample size calculations published between 2000 and 2008. Each trial was reviewed independently by two statisticians. The adequacy of the reporting of key elements in the CONSORT recommendations for CRTs was evaluated. Comparisons were made between the authors' a priori assumptions and values then observed in the trial. Of 166 trials, only 56 (34%) reported all key elements of sample size calculations in line with CONSORT recommendations. Elements specific to CRTs were the worst reported: the number of clusters or average cluster size was specified in only 94 (57%) and a measure of intracluster correlation coefficient (ICC) in only 86 (52%). Only 20 papers (12%) reported a priori and observed ICC values. In the majority of these reports, the a priori estimate for the ICC was conservative compared to the observed value. Few authors provided justifications for their choice of a priori estimates. Not unexpectedly, trials which reported no statistically significant difference were more likely to observe effect sizes smaller than the assumed clinically important difference. Even with the CONSORT extension to CRTs, the reporting of sample size calculations in CRTs remains below that necessary for transparent reporting. Further awareness is needed to encourage the reporting of observed ICCs in order to evaluate the choice of a priori estimates and interpret the trial results.
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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 | 0.314 | 0.742 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 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; 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".