Counting, analysing and reporting exacerbations of COPD in randomised controlled trials
Bibliographic record
Abstract
BACKGROUND: Clinical trials measure exacerbations of chronic obstructive pulmonary disease (COPD) inconsistently. A study was undertaken to determine if different methods for ascertaining and analysing COPD exacerbations lead to biased estimates of treatment effects. METHODS: Information on the methods used to count, analyse and report COPD exacerbation rates was abstracted from clinical trials of long-acting bronchodilators or long-acting bronchodilator/inhaled steroid combination products published between 2000 and 2006. Data from the Canadian Optimal Therapy of COPD Trial was used to illustrate how different analytical approaches can affect the estimate of exacerbation rates and their confidence intervals. RESULTS: 22 trials (17,156 patients) met the inclusion criteria and were reviewed. None of the trials adjudicated exacerbations or determined independence of events. 14/22 studies (64%) introduced selection bias by not analysing outcome data for subjects who prematurely stopped study medications. Only 31% of trials used time-weighted analyses to calculate the mean number of exacerbations/patient-year and only 15% accounted for between-subject variation. In the Canadian Optimal Therapy of COPD Trial the rate ratio for exacerbations/patient-year was 0.85 when all data were included in a time-weighted analysis, but was overestimated as 0.79 when data for those who prematurely stopped study medications were excluded and was further overestimated as 0.46 when a time-weighted analysis was not conducted; p values ranged from 0.03 to 0.24 depending on how exacerbations were determined and analysed. CONCLUSIONS: Clinical trials have used widely different methods to define and analyse COPD exacerbations and this can lead to biased estimates of treatment effects. Future trials should strive to include blinded adjudication and assessment of the independence of exacerbation events, and trials should report time-weighted intention-to-treat analyses with adjustments for between-subject variation in COPD exacerbations.
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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 | 0.703 | 0.908 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.019 |
| Bibliometrics | 0.026 | 0.027 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".