The merits of breaking the matches: a cautionary tale
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Matched-pair cluster randomization trials are frequently adopted as the design of choice for evaluating an intervention offered at the community level. However, previous research has demonstrated that a strategy of breaking the matches and performing an unmatched analysis may be more efficient than performing a matched analysis on the resulting data, particularly when the total number of communities is small and the matching is judged as relatively ineffective. The research concerning this question has naturally focused on testing the effect of intervention. However, a secondary objective of many community intervention trials is to investigate the effect of individual-level risk factors on one or more outcome variables. Focusing on the case of a continuous outcome variable, we show that the practice of performing an unmatched analysis on data arising from a matched-pair design can lead to bias in the estimated regression coefficient, and a corresponding test of significance which is overly liberal. However, for large-scale community intervention trials, which typically recruit a relatively small number of large clusters, such an analysis will generally be both valid and efficient. We also consider other approaches to testing the effect of an individual-level risk factor in a matched-pair cluster randomization design, including a generalized linear model approach that preserves the matching, a two-stage cluster-level analysis, and an approach based on generalized estimating equations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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