Comparison of statistical methods for analyzing cough frequency in a trial of refractory chronic cough
Bibliographic record
Abstract
Background: 24-hour cough frequency represents a common primary endpoint in clinical trials of refractory chronic cough (RCC). These data often exhibit skewness and overdispersion that are typically addressed using either log-transformation or a negative binomial distribution. No studies have compared how the choice of method impacts results. Objective: To compare these approaches in handling skewed and overdispersed cough frequencies and their effects on treatment interpretation. Methods: This was a post hoc analysis of data from a parallel-group, randomized, placebo-controlled trial evaluating mepolizumab in 30 patients with RCC and eosinophilic airways disease. The analysis compared a negative binomial generalized linear mixed effects model (GLMM) with untransformed data, and a linear mixed effects model (LMM) with log-transformed data. Both models specified the same fixed and random effects. Results: The negative binomial distribution captured lower cough frequencies but underestimated high cough frequencies. The Gaussian distribution on log-transformed data better represented peak densities and tails. The choice of model did not affect which covariates were significant. Compared to placebo, patients on mepolizumab had 42.4% greater 24-hour cough frequencies (95% CI -13.1% to 133.3%) at 14 weeks in the negative binomial GLMM versus 18.0% greater (95% CI -20.2% to 74.5%) in the LMM. Conclusion: Using data from a small trial, log-transformation reduced skewness and yielded more precise estimates than a negative binomial GLMM. The choice of method did not, however, affect the overall interpretation of treatment effects. A similar comparison should be performed in larger trials.
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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 | 0.202 | 0.400 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".