Determinants of tofacitinib discontinuation in adult patients with rheumatoid arthritis during long-term extension studies up to 9.5 years
Bibliographic record
Abstract
Abstract Objectives To examine determinants of tofacitinib discontinuation due to voluntary (i.e. patient-driven) or involuntary reasons (i.e. protocol mandated) in long-term extension (LTE) studies of patients with RA to inform clinical practice, clinical study execution and data capture. Methods This post hoc analysis used pooled data from patients receiving tofacitinib 5 or 10 mg twice daily (BID) in LTE studies. Outcomes included time to voluntary/involuntary discontinuation (and baseline predictors), including by geographic region. Exposure-adjusted event rates (EAERs) were calculated for adverse events (AEs), serious AEs (SAEs) and discontinuations due to AEs/SAEs. Results Of 4967 patients, 2463 (49.6%) discontinued [1552/4967 (31.2%) voluntarily, 911/4967 (18.3%) involuntarily] and 55 (1.1%) died over the course of 9.5 years. When involuntary discontinuation was present as a competing risk for voluntary discontinuation, patients who stayed on combination therapy and with higher patient-assessed pain were significantly more likely to discontinue for voluntary reasons (P < 0.05). Older patients, those enrolled in Asia, Europe or Latin America (vs USA or Canada) or with RF+/anti-CCP+ status were significantly less likely to discontinue for voluntary reasons (P < 0.05). Small numeric differences in disease activity were observed between geographic regions in patients who discontinued or completed the studies. EAERs were generally higher for tofacitinib 10 vs 5 mg BID, irrespective of discontinuation reason. Conclusion The factors associated with voluntary/involuntary discontinuation of tofacitinib suggest that treatment persistence in RA studies is partly predictable, which may be reflected in clinical practice. Applying these results may improve our understanding of attrition and inform future study design/execution. Trial registrations ClinicalTrials.gov (http://clinicaltrials.gov): NCT00413699 and NCT00661661.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".