Cost-Effectiveness Analysis of Ofatumumab for the Treatment of Relapsing-Remitting Multiple Sclerosis in Canada
Bibliographic record
Abstract
BACKGROUND: Ofatumumab is a high-efficacy disease-modifying therapy (DMT) approved for first-line treatment of relapsing-remitting multiple sclerosis (RRMS) in Canada. OBJECTIVE: The aim of this study was to evaluate the cost effectiveness of ofatumumab from a Canadian healthcare system perspective. METHODS: A Markov cohort model was run over 65 years using annual cycles, 1.5% annual discount rate, and 100% treatment discontinuation at 10 years. The British Columbia database informed natural history transition probabilities. Treatment efficacy for DMTs were sourced from a network meta-analysis. Clinical trial data were used to estimate probabilities for treatment-related adverse events. Health utilities and costs were obtained from Canadian sources (if available) and the literature. RESULTS: Among first-line indicated therapies for RRMS, ofatumumab was dominant (more effective, lower costs) over teriflunomide, interferons, dimethyl fumarate, and ocrelizumab. Compared with glatiramer acetate and best supportive care, ofatumumab resulted in incremental cost-effectiveness ratios (ICERs) of $24,189 Canadian dollars per quality-adjusted life-year (QALY) and $28,014/QALY, respectively. At a willingness-to-pay threshold of $50,000/QALY, ofatumumab had a 64.3% probability of being cost effective. Among second-line therapies (scenario analysis), ofatumumab dominated natalizumab and fingolimod and resulted in an ICER of $50,969 versus cladribine. CONCLUSIONS: Ofatumumab is cost effective against all comparators and dominant against all currently approved and reimbursed first-line DMTs for RRMS, except glatiramer acetate.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".