Mega Trials in COPD—Clinical Data Analysis and Design Issues
Bibliographic record
Abstract
The TORCH and UPLIFT randomised controlled trials have provided important data on the benefits of COPD treatments, but also some lessons in study design and data analysis that we will here review. Firstly, it is fundamental that the study question be answerable by the study design. The question in the TORCH study was aimed at a comparison with 'usual care', but the placebo group was not 'usual care'. Secondly, TORCH and UPLIFT were among the first trials to follow the intent-to-treat principle, fundamental to avoid bias in randomised trials. However, this principle was followed for the mortality outcome, but not for lung function, so that the findings related to lung function decline are subject to bias from regression to the mean. Finally, a re-analysis of the TORCH study (performed to fully exploit the data as a 2 × 2 factorial trial) shows that a mortality benefit is entirely accounted for by the effect of the long-acting beta-agonist salmeterol, with no effect attributable to the inhaled corticosteroid fluticasone component of the combination therapy. Together, these data suggest that long-acting bronchodilators, including anticholinergics such as tiotropium and beta-agonists, are associated with lower mortality of patients with COPD, but not inhaled corticosteroids. With COPD one of the major causes of morbidity and mortality worldwide, mega trials such as TORCH and UPLIFT are much needed, but must achieve the utmost scientific rigour in their design and analysis.
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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 | 0.604 | 0.787 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.010 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".