Methods for modelling change in cluster randomization trials
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Bibliographic record
Abstract
Randomized trials are often designed to assess an intervention's ability to change patient knowledge, behaviour or health. The study outcome will then need to be measured at least twice for each subject--prior to random assignment and following implementation of the intervention. In this paper we consider methods for modelling change when data are obtained from cluster randomization trials where the unit of allocation is a family, school or community. Attention focuses on mixed effects linear regression extensions of (i) two-sample t-tests and (ii) analysis of covariance, in both cases accounting for dependencies among cluster members. Algebraic expressions for tests of the intervention effect are derived for the special case where there are a fixed number of subjects per cluster while simulation studies are used to compare the power of these procedures in the more realistic case where there is variability in cluster size. A key conclusion is that there can be considerable gains in power when allowing for different individual-level and cluster-level associations between the baseline and follow-up assessments. The discussion is illustrated using data from a school-based smoking prevention 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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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