Cost-effectiveness of Four Immunomodulatory Therapies for Relapsing-Remitting Multiple Sclerosis: A Markov Model Based on Long-term Clinical Data
Bibliographic record
Abstract
BACKGROUND: Before the introduction of the immunomodulatory therapies for multiple sclerosis (MS), treatment options for MS consisted of symptomatic management (physical therapy and pharmacological treatment for symptom management). Symptomatic management for MS has been supplemented in the past decade by 2 new classes of immunomodulatory therapies that have been approved as first-line treatments for relapsing-remitting multiple sclerosis (RRMS): subcutaneous glatiramer acetate (SC GA) and 3 beta-interferons: intramuscular interferon beta-1a (IM IFNbeta-1a), SC IFNbeta-1a, and SC IFNbeta-1b. OBJECTIVE: To estimate the economic outcomes of 5 treatment strategies: symptom management alone, symptom management combined with SC GA, IM IFNbeta1-a, SC IFNbeta1-a, or SC IFNbeta1-b in patients diagnosed with RRMS. METHODS: A literature-based Markov model was developed to assess the cost-effectiveness of 5 treatment strategies for managing a hypothetical cohort of patients diagnosed with RRMS in the United States--4 immunomodulatory drug therapies and symptom management alone. Health states were based on the Kurtzke Expanded Disability Status Scale (EDSS), a widely accepted scale for assessing RRMS (higher EDSS scores = increased disease severity). Baseline relapse and disease progression transition probabilities for symptom management were obtained from natural history studies. Treatment effects of the immunomodulatory therapies were estimated by applying a percentage reduction to the symptom management transition probabilities for relapse (27% reduction) and disease progression (30% reduction). Transition probabilities were subsequently adjusted to account for (1) the effects of neutralizing antibodies, specifically on relapse rates by assuming no additional therapy benefits after the second year of continuous therapy, and (2) treatment discontinuation. Therapy-specific data were obtained from clinical trials and long-term follow-up observational studies. Transitions among health states occurred in 1-month cycles for the lifetime of a patient. Costs (2005 US$) and outcomes were discounted at 3% annually. RESULTS: The incremental cost per quality-adjusted life-year for the 4 immunomodulatory therapies is $258,465, $303,968, $416,301, and $310,691 for SC GA, IM IFNbeta-1a, SC IFNbeta-1a, and SC IFNbeta-1b, respectively, compared with symptom management alone. Sensitivity analyses demonstrated that results were sensitive to changes in utilities, disease progression rates, time horizon, and immunomodulatory therapy cost. CONCLUSIONS: The pharmacoeconomic model determined that SC GA was the best strategy of the 4 immunomodulatory therapies used to manage MS and resulted in better outcomes than symptom management alone. Sensitivity analyses indicated that the model was sensitive to changes in a number of key parameters, and thus changes in these key parameters would likely influence the estimated cost-effectiveness results. Head-to-head randomized clinical trials comparing the immunomodulatory therapies for the treatment of MS are necessary to validate the projections from the pharmacoeconomic analyses, particularly since the results available today from the clinical trials do not account adequately for treatment dropouts.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".