Statistical methods for the meta-analysis of cluster randomization trials
Bibliographic record
Abstract
Cluster randomization trials have become a very attractive research strategy, particularly for the evaluation of health service interventions. The need to conduct meta-analyses of such trials is also becoming more common. However, as with cluster randomization trials in general, such analyses raise special methodologic challenges. In this paper, we discuss and illustrate several statistical approaches that might be applied to a meta-analysis of cluster randomization trials, each of which has a binary endpoint. Statistical methods for constructing inferences for a summary intervention odds ratio include those based on Mantel-Haenszel procedures, the ratio estimator approach, Woolf procedures and generalized estimating equations using robust variance estimation. The advantages and disadvantages of each method are discussed in the context of an example.
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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 | 0.236 | 0.518 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.017 | 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".