Tofacitinib Persistence in Patients with Rheumatoid Arthritis: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To compare medication persistence of tofacitinib with persistence of injectable biological disease-modifying antirheumatic drugs (bDMARD) in patients with rheumatoid arthritis (RA). METHODS: We performed a retrospective new-user cohort study of patients with RA in the IBM MarketScan Research Databases. New users of tofacitinib or bDMARD were identified between November 2012 and December 2016. Persistence, in number of years, was the time between treatment initiation and the earliest occurrence of discontinuation or switching from the medication prescribed at cohort entry. Persistence of tofacitinib was compared with bDMARD persistence using Cox proportional hazards regression with adjustment for high-dimensional propensity scores. Similar methods were used for an analysis of post first-line therapy in patients who switched to tofacitinib from a bDMARD. RESULTS: New tofacitinib users (n = 1031) were 56 years of age, on average, and 82% were women. New bDMARD users (n = 17,803) were 53 years of age, on average, and 78% were women. New tofacitinib users had shorter medication persistence (median 0.81 yrs) compared to bDMARD patients (1.02 yrs). After adjustment, the HR for discontinuation of tofacitinib compared with bDMARD was 1.14 (95% CI 1.05-1.25). Patients who switched to tofacitinib from a bDMARD had longer persistence than patients who switched to a bDMARD (adjusted HR for discontinuation 0.90, 95% CI 0.83-0.97). CONCLUSION: Further research is warranted to understand the reasons for discontinuation of tofacitinib despite its ease of administration and to understand the observed differences between switchers and new users.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".