Bibliographic record
Abstract
PURPOSE: Observational studies using computerized healthcare databases have become popular to investigate the potential effectiveness of old drugs for new indications. Many of these studies reporting remarkable effectiveness were shown to be affected by different time-related biases. We describe these biases and illustrate their effects using a cohort of patients treated for chronic obstructive pulmonary disease (COPD). METHODS: The Quebec healthcare databases were used to form a cohort of 124 030 patients with COPD, 50 years or older, treated between 2000 and 2015. Inhaled corticosteroids (ICS) and long-acting bronchodilators were used as exposures, with diverse outcomes, including lung cancer, acute myocardial infarction and death, to illustrate protopathic, latency time, immortal time, time-window, depletion of susceptibles, and immeasurable time biases. RESULTS: Protopathic bias affected bronchodilator-defined cohort entry with an incident rate of lung cancer of 23.9 per 1000 in the first year, compared with around 12.0 in the subsequent years. When latency and immortal times were misclassified, ICS were associated with decreased incidence of lung cancer (hazard ratio [HR] 0.32; 95% CI: 0.30-0.34), compared with 0.50 (95% CI: 0.48-0.53) after correcting for immortal time bias and 0.96 (95% CI: 0.91-1.02) after also correcting for latency time bias. Time-window, depletion of susceptibles and immeasurable time biases also affected the findings similarly. CONCLUSIONS: Many observational studies of new indications for older drugs reporting unrealistic effectiveness were affected by avoidable time-related biases. The apparent effectiveness often disappears with proper design and analysis. Future studies should consider these time-related issues to avoid severely biased results.
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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 | 0.116 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".