Modeling the cost-effectiveness of a new treatment for MS (natalizumab) compared with current standard practice in Sweden
Bibliographic record
Abstract
OBJECTIVE: To estimate the cost-effectiveness of a new treatment (natalizumab) for multiple sclerosis (MS) compared with current standard therapy with disease-modifying drugs (DMDs) in Sweden. METHODS: A Markov model was constructed to illustrate disease progression based on functional disability (the Expanded Disability Status Scale (EDSS)). The effectiveness of natalizumab was based on a 2-year clinical trial in 942 patients (AFFIRM). The effectiveness of current DMDs was estimated from a matched sample of 512 patients in the Stockholm MS registry. Patients withdrawing from treatment were assumed to follow the disease course of 824 patients with relapsing-remitting disease at onset in the Ontario natural history cohort. Costs and utilities are based on a recent observational study in 1339 patients. All data sets were available at the patient level. Main results are presented from the societal perspective, over a 20-year time frame, in 2005 Euros (euro1 = 9.25 SEK). RESULTS: In the base case, treatment with natalizumab was less expensive and more effective than treatment with current DMDs. When only healthcare costs were considered, the cost per quality-adjusted life year gained with natalizumab was euro38 145. Results are sensitive only to the time horizon of the analysis and assumptions about effectiveness of natalizumab beyond the trial. CONCLUSIONS: This cost-effectiveness analysis used registry data, cohort and observational studies to extrapolate the efficacy findings of natalizumab from the AFFIRM clinical trial to measure effectiveness in clinical practice. The analysis results suggest that for the population considered, natalizumab provides an additional health benefit at a similar cost to current DMDs from a societal perspective.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".