Allocation techniques for balance at baseline in cluster randomized trials: a methodological review
Why this work is in the frame
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Bibliographic record
Abstract
Reviews have repeatedly noted important methodological issues in the conduct and reporting of cluster randomized controlled trials (C-RCTs). These reviews usually focus on whether the intracluster correlation was explicitly considered in the design and analysis of the C-RCT. However, another important aspect requiring special attention in C-RCTs is the risk for imbalance of covariates at baseline. Imbalance of important covariates at baseline decreases statistical power and precision of the results. Imbalance also reduces face validity and credibility of the trial results. The risk of imbalance is elevated in C-RCTs compared to trials randomizing individuals because of the difficulties in recruiting clusters and the nested nature of correlated patient-level data. A variety of restricted randomization methods have been proposed as way to minimize risk of imbalance. However, there is little guidance regarding how to best restrict randomization for any given C-RCT. The advantages and limitations of different allocation techniques, including stratification, matching, minimization, and covariate-constrained randomization are reviewed as they pertain to C-RCTs to provide investigators with guidance for choosing the best allocation technique for their trial.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.733 | 0.979 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.055 | 0.007 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it