Cost-effectiveness of fingolimod versus interferon-β1a for the treatment of pediatric-onset multiple sclerosis in Canada
Bibliographic record
Abstract
AIMS: To evaluate the cost-effectiveness of fingolimod versus interferon (IFN)-β1a at a dose of 30 μg per week for the treatment of relapsing pediatric-onset multiple sclerosis (POMS) in Canada. MATERIAL AND METHODS: A discrete-time Markov model was developed to compare fingolimod with IFN β-1a over a time horizon of two years representing patients followed up to mean age of 18 years from a Canadian health care system perspective. Twenty-one health states based on the Expanded Disability Status Scale (EDSS) were considered: EDSS 0‒9 for relapsing multiple sclerosis (MS), EDSS 0‒9 for secondary progressive MS, and "Death." Relative treatment efficacy for fingolimod versus IFN-β1a was estimated from the PARADIGMS study. Costs and resource use were obtained from published literature and Canadian sources. Utilities were estimated by mapping the Pediatric Quality of Life inventory data onto the Child Health Utility Index-9 Dimension using a published mapping algorithm. Future costs and benefits were discounted at 1.5% per annum. RESULTS: Compared with IFN β-1a, fingolimod led to an increase in quality-adjusted life-years (QALYs) (0.125) with incremental costs (Canadian dollars [CAD] 2,977) and to an incremental cost-effectiveness ratio (ICER) of CAD 23,886/QALY over a time horizon of two years representing patients followed up to mean age of 18 years. The monetary benefits of fingolimod treatment versus IFN β-1a at a willingness-to-pay (WTP) threshold of CAD 50,000 per QALY gained were higher than the costs. One-way sensitivity analysis and probabilistic sensitivity analysis (PSA) both confirmed the robustness of the results. LIMITATIONS: The main limitations of this analysis primarily stem from the limited data availability in POMS. CONCLUSIONS: Fingolimod is cost effective compared with IFN β-1a for the treatment of POMS over a time horizon of two years representing patients followed up to a mean age of 18 years in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".