Comparative efficacy of ofatumumab <i>versus</i> oral therapies for relapsing multiple sclerosis patients using propensity score analyses and simulated treatment comparisons
Bibliographic record
Abstract
Background: Evidence from network meta-analyses (NMAs) and real-world propensity score (PS) analyses suggest monoclonal antibodies (mAbs) offer a therapeutic advantage over currently available oral therapies and, therefore, warrant consideration as a distinct group of high-efficacy disease-modifying therapies (DMTs) for patients with relapsing multiple sclerosis (RMS). This is counter to the current perception of these therapies by some stakeholders, including payers. Objectives: A multifaceted indirect treatment comparison (ITC) approach was undertaken to clarify the relative efficacy of mAbs and oral therapies. Design: the oral therapies cladribine, fingolimod, and ozanimod. Data sources and methods: As IPD were available for trials of ofatumumab and fingolimod, PS analyses were conducted. Given summary-level data were available for cladribine, fingolimod, and ozanimod trials, STCs were conducted between ofatumumab and each of these oral therapies. Three efficacy outcomes were compared: annualized relapse rate (ARR), 3-month confirmed disability progression (3mCDP), and 6-month CDP (6mCDP). Results: The PS analyses demonstrated ofatumumab was statistically superior to fingolimod for ARR and time to 3mCDP but not time to 6mCDP. In STCs, ofatumumab was statistically superior in reducing ARR and decreasing the proportion of patients with 3mCDP compared with cladribine, fingolimod, and ozanimod and in decreasing the proportion with 6mCP compared with fingolimod and ozanimod. These findings were largely consistent with recently published NMAs that identified mAb therapies as the most efficacious DMTs for RMS. Conclusion: Complementary ITC methods showed ofatumumab was superior to cladribine, fingolimod, and ozanimod in lowering relapse rates and delaying disability progression among patients with RMS. Our study supports the therapeutic superiority of mAbs over currently available oral DMTs for RMS and the delineation of mAbs as high-efficacy therapies.
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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 | 0.050 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".