Meta-Analyses of Cluster Randomization Trials
Bibliographic record
Abstract
A commonly cited purpose for conducting a meta-analysis of randomized trials is to increase the statistical power for detecting the effect of an intervention on a specified set of endpoints. At the same time, it also has been noted by several authors that many large-scale cluster randomization trials have not had the power to detect small or even moderate effect sizes. The loss of efficiency associated with cluster randomization relative to individual randomization, and the frequent failure of investigators to take this loss of efficiency into account at the planning stage of a trial, undoubtedly contributes to this problem. In this article, the authors present an approach that may be used to estimate the power of a planned meta-analysis that includes trials that are cluster randomized. Two examples are presented.
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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 | 0.227 | 0.542 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.027 | 0.048 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".