Performance of methods for analyzing continuous data from stratified cluster randomized trials – A simulation study
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
Background: The adoption of cluster randomized trials (CRTs) with the stratified design is currently gaining widespread interest. In the stratified design, clusters are first grouped into two or more strata and then randomized into treatment groups within each stratum. In this study, we evaluated the performance of several commonly used methods for analyzing continuous data from stratified CRTs. Methods: This is a simulation study where we compared four methods: mixed-effects, generalized estimating equation (GEE), cluster-level (CL) linear regression and meta-regression methods to analyze the continuous data from stratified CRTs using a simulation study with varying numbers of clusters, cluster sizes, intra-cluster correlation coefficients (ICCs) and effect sizes. This study was based on a stratified CRT with one stratification variable with two strata. The performance of the methods was evaluated in terms of the type I error rate, empirical power, root mean square error (RMSE), and width and coverage of the 95% confidence interval (CI). Results: GEE and meta-regression methods had high type I error rates, higher than 10%, for the small number of clusters. All methods had similar accuracy, measured through RMSE, except meta-regression. Similarly, all methods but meta-regression had similar widths of 95% CIs for the small number of clusters. For the same sample size, the empirical power for all methods decreased as the value of the ICC increased. Conclusion: In this study, we evaluated the performance of several methods for analyzing continuous data from stratified CRTs. Meta-regression was the least efficient method compared to other methods.
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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 | 0.254 | 0.615 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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