The cost-effectiveness of delayed-release dimethyl fumarate for the treatment of relapsing-remitting multiple sclerosis in Canada
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) causes significant disability and diminished quality-of-life. Delayed-release dimethyl fumarate (DMF; also known as gastro-resistant DMF) is a new oral treatment for relapsing-remitting MS (RRMS) approved in the US, Australia, Canada, and Europe. OBJECTIVES: A cost-effectiveness model was developed to compare the health economic impact of DMF against other disease-modifying therapies (DMTs) as first-line RRMS treatment from a Canadian Ministry of Health perspective. METHODS: A Markov cohort model was developed to simulate patients' progression through health states based on the Kurtzke Expanded Disability Status Scale (EDSS) over a life-time horizon. Patients entered the model based on a distribution of baseline EDSS scores, from which they could progress to higher or regress to lower EDSS state, or remain in the same state. Relapses could occur at any EDSS score. Results from a mixed-treatment comparison were used to inform model inputs for disease progression and relapse rates per treatment. Costs included direct medical costs stratified by EDSS score. Utilities were accrued based on time spent in each EDSS state. RESULTS: Compared with glatiramer acetate, DMF yielded 0.528 incremental quality-adjusted life-years (QALYs) at an incremental cost of $23 338 Canadian dollars (CAD), resulting in an incremental cost-effectiveness ratio (ICER) of CAD $44 118/QALY. The ICER for DMF compared with Rebif 44 mcg was CAD $10 672. Results were consistent across a wide range of one-way and probabilistic sensitivity analyses. CONCLUSIONS: Based on traditional cost-effectiveness thresholds in Canada (CAD $50 000-60 000), DMF can be considered a cost-effective option compared to other first-line DMTs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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".