Sensitivity of methods for analyzing continuous outcome from stratified cluster randomized trials – an empirical comparison study
Bibliographic record
Abstract
The assessment of the sensitivity of statistical methods has received little attention in cluster randomized trials (CRTs), especially for stratified CRT when the outcome of interest is continuous. We empirically examined the sensitivity of five methods for analyzing the continuous outcome from a stratified CRT - aimed to investigate the efficacy of the Classroom Communication Resource (CCR) compared to usual care to improve the peer attitude towards children who stutter among grade 7 students. Schools - the clusters, were divided into quintile based on their socio-political resources, and then stratified by quintile. The schools were then randomized to CCR and usual care groups in each stratum. The primary outcome was Stuttering Resource Outcomes Measure. Five methods, including the primary method, were used in this study to examine the effect of CCR. The individual-level methods were: (i) linear regression; (ii) mixed-effects method; (iii) GEE with exchangeable correlation structure (primary method of analysis). And the cluster-level methods were: (iv) cluster-level linear regression; and (v) meta-regression. These methods were also compared with or without adjustment for stratification. Ten schools were stratified by quintile, and then randomized to CCR (223 students) and usual care (231 students) groups. The direction of the estimated differences was same for all the methods except meta-regression. The widths of the 95% confidence intervals were narrower when adjusted for stratification. The overall conclusion from all the methods was similar but slightly differed in terms of effect estimate and widths of confidence intervals. TRIALREGISTRATION: Clinicaltrials.gov, NCT03111524. Registered on 9 March 2017.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.740 | 0.870 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".