Cost-effectiveness of Four Immunomodulatory Therapies for Relapsing-Remitting Multiple Sclerosis: A Markov Model Based on Long-term Clinical Data
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».