Methods for Data Analysis in Split-mouth Randomized Clinical Trials, a Simulation Study
Bibliographic record
Abstract
Split-mouth trials are a design of randomized controlled trial in dentistry in which divisions of the mouth are the units of randomization. Since there is more than one tooth in each mouth division, the structure of the data is complex, which can create difficulties in the statistical analysis. The aim of this study was to determine what is the most appropriate method to analyze split-mouth trials with continuous outcomes, with regards to the treatment effect estimates, power, type-I error, confidence interval coverage and confidence interval width. A superiority split-mouth trial in the field of periodontology was simulated, using two mouth divisions and varying underlying study characteristics such as correlation among teeth, treatment effects and sample size. Twenty-four statistical methods were compared across 315 scenarios. The performance of the statistical methods depended mainly on the correlation among the data, and a paired t-test performed the best across the different scenarios.
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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 | 0.443 | 0.586 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".