Application of a Generalized Random Effects Regression Model for Cluster-correlated Longitudinal Data to a School-based Smoking Prevention Trial
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
In cluster-randomized trials, groups of subjects (clusters) are assigned to treatments, whereas observations are taken on the individual subjects. Since observations on subjects in the same cluster are typically more similar than observations from different clusters, analyses of such data must take intracluster correlation into account rather than assuming independence among all observations. Random effects models are useful for this purpose. The problem becomes more complicated if, in addition, repeated observations are taken on subjects over time. This introduces intraindividual correlation, which is typical for longitudinal studies. The Waterloo Smoking Prevention Project, study 3 (WSPP3), 1989-1996, is a study giving rise to cluster-correlated longitudinal data, where schools were randomized to either a smoking intervention program or to a control condition. Smoking status was assessed on grade 6 students in these schools, with annual follow-up observations throughout elementary and high school years. The authors illustrate the use of a generalized random effects model for analyzing this type of data. This model obtains appropriate estimates and standard errors for both individual-level covariates and those at the level of the cluster.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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