Comparative efficacy of diroximel fumarate, ozanimod and interferon beta-1a for relapsing multiple sclerosis using matching-adjusted indirect comparisons
Bibliographic record
Abstract
Aim: Diroximel fumarate (DRF), ozanimod (OZA) and interferon beta-1a (IFN) are disease-modifying therapies approved for the treatment of relapsing multiple sclerosis. No randomized trials have compared DRF versus OZA and IFN. We compared DRF versus OZA and DRF versus IFN using matching-adjusted indirect comparisons for efficacy outcomes, including annualized relapse rate (ARR), 12- and 24-week confirmed disability progression (CDP) and absence of gadolinium-enhancing (Gd+) T1 lesions and new/newly enlarging T2 lesions. Patients & methods: We used individual patient data from EVOLVE-MS-1 ( NCT02634307 ), a 2-year, open-label, single-arm, phase III study of DRF (n = 1057) and aggregate data from RADIANCE ( NCT02047734 ), a 2-year, double-blind, phase III study that compared OZA 1 mg once daily (n = 433) and intramuscular IFN 30 μg once weekly (n = 441). To account for cross-trial differences, the EVOLVE-MS-1 population was restricted to those who met the inclusion/exclusion criteria for RADIANCE, then weighted to match the average baseline characteristics of RADIANCE. Results: After weighting, DRF and OZA had similar ARRs (0.18 and 0.17, respectively), with a rate difference (DRF vs OZA) of 0.01 (95% confidence interval [CI]: -0.04 to 0.06). DRF had a lower ARR than IFN (0.18 and 0.28, respectively), with a rate difference (DRF vs IFN) of -0.10 (95% CI: -0.16 to -0.04) after weighting. Outcomes for 12- and 24-week CDP favored DRF versus OZA; 12-week CDP favored DRF versus IFN, but there was not strong evidence favoring DRF over IFN for 24-week CDP. Compared with OZA and IFN, DRF had higher proportions of patients without Gd+ T1 lesions and patients without new/newly enlarging T2 lesions. Conclusion: Disability progression and radiological outcomes were favorable for DRF versus OZA, although no differences were observed in ARR. Clinical and radiological outcomes generally favored DRF versus IFN. These findings may be informative for patients and clinicians considering different treatment options for MS.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".