Inhaled steroids and mortality in COPD: bias from unaccounted immortal time
Bibliographic record
Abstract
A recent observational study, which suggested that inhaled corticosteroids (ICS) with or without long-acting bronchodilators are effective at reducing all-cause mortality in chronic obstructive pulmonary disease (COPD) patients, may be subject to immortal time bias. This bias was assessed using a population-based cohort of 3,524 newly treated COPD patients from Saskatchewan, Canada, observed from 1990-1999. Regular users of bronchodilators or ICS were followed for 3 yrs, during which time 860 deaths occurred. Cox's proportional hazards model was used to compare the hierarchical intention-to-treat approach employed in the recent study, a technique subject to bias from two sources of immortal time, with the conventional intention-to-treat approach and the according-to-treatment approach. The adjusted rate ratio of death using the hierarchical intention-to-treat approach was 0.66 (95%) confidence interval (CI) 0.57-0.76) for ICS use relative to bronchodilator use, compared with 0.75 (95% CI 0.62-0.90) with the conventional intention-to-treat approach. Conversely, the rate ratio was 0.94 (95% CI 0.81-1.09) with the according-to-treatment approach, which accounts for both sources of immortal time. In this study, regular inhaled corticosteroid use in chronic obstructive pulmonary disease was not found to reduce all-cause mortality. Suggestion of this benefit from a previous observational study is the result of bias from unaccounted immortal time in its cohort design and analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.125 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".