Comparing New-User Cohort Designs: The Example of Proton Pump Inhibitor Effectiveness in Idiopathic Pulmonary Fibrosis
Bibliographic record
Abstract
The prevalent new-user cohort design is useful for assessing the effectiveness of a medication in the absence of an active comparator. Alternative approaches, particularly in the presence of informative censoring, include a variant of this design based on never users of the study drug and the marginal structural Cox model approach. We compared these approaches in assessing the effectiveness of proton pump inhibitors (PPIs) in reducing mortality among patients with idiopathic pulmonary fibrosis (IPF) using a cohort of IPF patients identified in the United Kingdom's Clinical Practice Research Datalink and diagnosed between 2003 and 2016. The cohort included 2,944 IPF patients, 1,916 of whom initiated use of PPIs during follow-up. There were 2,136 deaths (mortality rate = 25.8 per 100 person-years). Using the conventional prevalent new-user design, we found a hazard ratio for death associated with PPI use compared with nonuse of 1.07 (95% confidence interval (CI): 0.94, 1.22). The variant of the prevalent new-user design comparing PPI users with never users found a hazard ratio of 0.82 (95% CI: 0.73, 0.91), while the marginal structural Cox model found a hazard ratio of 1.08 (95% CI: 0.85, 1.38). The marginal structural model and the conventional prevalent new-user design, both accounting for informative censoring, produced similar results. However, the prevalent new-user design variant based on never users introduced selection bias and should be avoided.
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 | 0.301 | 0.409 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".