Effectiveness of Aspirin in COPD: Biases in the Observational Studies
Bibliographic record
Abstract
Several observational studies report decreased incidence of mortality and of exacerbations with aspirin use in patients with chronic obstructive pulmonary disease (COPD), with calls for a large randomized trial. Aspirin does have local and systemic pulmonary mechanisms of action that could make this drug beneficial in the treatment of COPD. However, the potential for biases in the observational studies has not been examined. We searched the literature for all observational studies reporting on the effect of aspirin in COPD patients on exacerbation and mortality. We reviewed the studies for the presence of time-related and other biases. We identified eight observational studies reporting an overall reduction in all-cause mortality or exacerbation with aspirin use of 21% (pooled rate ratio (RR) 0.79; 95% CI 0.71-0.86). We found two studies affected by immortal time bias (pooled RR 0.81; 95% CI 0.74-0.89), three studies affected by collider-stratification bias (pooled RR 0.66; 95% CI 0.55-0.79) and three that involved some exposure misclassification (pooled RR 0.85; 95% CI 0.78-0.92). Moreover, while adjusting for cardiovascular factors, six of the eight studies did not adjust for important markers of COPD severity and thus remain susceptible to confounding bias. In conclusion, all observational studies reporting on the effectiveness of aspirin on major outcomes of COPD are affected by biases known to exaggerate the effectiveness of a drug. As these studies cannot be used to support a beneficial effect for aspirin in COPD, it would be premature to consider a randomized trial to investigate this question until methodologically rigorous studies are available.
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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 | 0.491 | 0.719 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".