Re-evaluating Safety and Effectiveness of Dabigatran Versus Warfarin in a Nationwide Data Environment: A Prevalent New-User Design Study
Bibliographic record
Abstract
INTRODUCTION: The new user cohort design is widely used to assess the effects of a new drug, such as dabigatran, but inherently excludes some users due to prior use of the comparator drug, for example warfarin. The prevalent new-user design offers a solution that includes all eligible users of the new drug. OBJECTIVE: To evaluate the safety and effectiveness of dabigatran versus warfarin in non-valvular atrial fibrillation (NVAF) patients with prevalent new-user design. METHODS: Taiwan National Health Insurance and mortality data from 2011 through 2015 were utilized. From an incident NVAF cohort, we identified dabigatran initiators as either incident or prevalent (switchers from warfarin) new users. Time- and prescription-based exposure sets were formed for dabigatran initiators to account for prior warfarin prescriptions. A comparable warfarin user was matched on the time-conditional propensity score to the dabigatran initiator in each set. The matched patients were followed for clinical outcomes, with Cox proportional hazards model used to estimate hazard ratios (HRs). RESULTS: There were 10,811 dabigatran initiators, including 22% prevalent new users (switchers), who formed the exposure sets and were matched 1:1 to warfarin users. Dabigatran use was associated with lower risks of intracranial hemorrhage (HR 0.51; 95% confidence interval [CI] 0.39, 0.66) and gastrointestinal bleeding (HR 0.81; 95% CI 0.70, 0.92), compared with warfarin use. These effects were similar between the incident and prevalent new users. CONCLUSION: Using a design that includes both incident and prevalent new users of dabigatran, the use of dabigatran is associated with lower major bleeding risk than warfarin use among patients with incident NVAF.
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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 | 0.098 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".