Cost-utility analysis of interferon beta-1B in secondary progressive multiple sclerosis using natural history disease data.
Bibliographic record
Abstract
OBJECTIVES: Different cost-effectiveness analyses have been presented for interferon beta-1b (IFNB) in the treatment of secondary progressive multiple sclerosis (SPMS). All studies have used modeling techniques since any effect on progression of disability achieved during a clinical trial will last beyond the trial. Different approaches to extrapolation have been taken, but generally they have been based on disease progression and relapse rates in clinical trials. The problem with this approach is that the population in clinical trials is a selected group of patients, which has the potential to bias results. A better method for extrapolation is to use epidemiologic data. The objective of this study is to incorporate natural history data for MS into a previously presented cost-utility model and to compare the two methods of extrapolation. METHODS: Clinical trial data were used to model disease progression during the first 3 years in the model. To extrapolate beyond the trial (10 years), data on progression of disability were available from a geographically based epidemiologic study of the natural history of MS in Canada. There were 568 patients who had converted to SPMS during the follow-up that were included in the data set. Mean costs and utilities for each Markov state were calculated from a population-based cross-sectional study in Sweden. RESULTS: The extrapolation using clinical trial data appears to have underestimated the progression of disability in the long term and thus also the potential benefit of treatment. Using the epidemiologic data, the incremental cost per QALY is SEK 257,000 (US $25,700; US $1 = SEK 10; November 2000) when all costs (direct, informal care, and indirect) are included (discounted 3%). This compares to SEK 342,000 in the previous model. The lower cost-effectiveness ratio is mostly due to a larger QALY gain with treatment than in the previous model (0.217 compared with 0.162). CONCLUSIONS: Cost-effectiveness analysis in SPMS requires that the effect of treatment beyond clinical trials be included. The method of extrapolation clearly affects the results, and when available, epidemiologic data should be used. Using the longitudinal data from Canada, the cost-utility ratios for IFNB-1b in the treatment of SPMS appear well within the acceptable range.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".