BACKGROUND: The safety and efficacy of disease-modifying therapies (DMTs) for relapsing-remitting multiple sclerosis (RRMS) has been established; however, it is not clear which provides optimal value, given benefit-risk profiles and costs. AIMS: To compare the cost-effectiveness of current DMTs for patients with RRMS in the US. MATERIALS AND METHODS: A Markov model predicting RRMS course following initiation of a DMT was created comparing outcomes (e.g. relapses, disease progression) and costs of natalizumab (NTZ), dimethyl fumarate (DMF), and peginterferon beta-1a (PEG) with fingolimod (FIN), glatiramer acetate (GA, 20 mg daily), and subcutaneous interferon beta-1a (IFN, 44 mcg), respectively, over 10 years. RRMS and secondary-progressive MS (SPMS) EDSS state transitions were predicted in 3-month cycles in which patients were at risk of death, relapse, or discontinuation. Upon DMT discontinuation, natural history progression and relapse rates were applied. Incremental cost-effectiveness ratios (ICERs) were estimated for the cost per relapse avoided, relapse-free years gained, progression avoided, and progression-free years gained. The impact of model parameters on outcomes was evaluated via one-way sensitivity analyses. RESULTS: Costs ranged from $561,177 (NTZ) to $616,251 (GA). NTZ, DMF, and PEG were dominant (less costly and more effective) compared to FIN, GA, and IFN, respectively, for all ICERs. Variability in drug costs and parameters that affected drug cost accrual (e.g. discontinuation rates and the decision to drop out after SPMS conversion) had a considerable impact on ICERs. LIMITATIONS: Several simplifying assumptions were made that may represent potential limitations of this analysis (e.g. a constant treatment effect over time was assumed). CONCLUSIONS: The results from this analysis suggest that the NTZ, DMF, and PEG are cost-effective DMT choices compared to FIN, GA, and IFN, respectively. The actual impact on a particular plan will vary based on drug pricing and other factors affecting drug cost accrual.
BACKGROUND: The safety and efficacy of disease-modifying therapies (DMTs) for relapsing-remitting multiple sclerosis (RRMS) has been established; however, it is not clear which provides optimal value, given benefit-risk profiles and costs. AIMS: To compare the cost-effectiveness of current DMTs for patients with RRMS in the US. MATERIALS AND METHODS: A Markov model predicting RRMS course following initiation of a DMT was created comparing outcomes (e.g. relapses, disease progression) and costs of natalizumab (NTZ), dimethyl fumarate (DMF), and peginterferon beta-1a (PEG) with fingolimod (FIN), glatiramer acetate (GA, 20 mg daily), and subcutaneous interferon beta-1a (IFN, 44 mcg), respectively, over 10 years. RRMS and secondary-progressive MS (SPMS) EDSS state transitions were predicted in 3-month cycles in which patients were at risk of death, relapse, or discontinuation. Upon DMT discontinuation, natural history progression and relapse rates were applied. Incremental cost-effectiveness ratios (ICERs) were estimated for the cost per relapse avoided, relapse-free years gained, progression avoided, and progression-free years gained. The impact of model parameters on outcomes was evaluated via one-way sensitivity analyses. RESULTS: Costs ranged from $561,177 (NTZ) to $616,251 (GA). NTZ, DMF, and PEG were dominant (less costly and more effective) compared to FIN, GA, and IFN, respectively, for all ICERs. Variability in drug costs and parameters that affected drug cost accrual (e.g. discontinuation rates and the decision to drop out after SPMS conversion) had a considerable impact on ICERs. LIMITATIONS: Several simplifying assumptions were made that may represent potential limitations of this analysis (e.g. a constant treatment effect over time was assumed). CONCLUSIONS: The results from this analysis suggest that the NTZ, DMF, and PEG are cost-effective DMT choices compared to FIN, GA, and IFN, respectively. The actual impact on a particular plan will vary based on drug pricing and other factors affecting drug cost accrual.
Authors: Timothy Spelman; William L Herring; Thibaut Dort; Yuanhui Zhang; Michael Tempest; Isobel Pearson; Ulrich Freudensprung; Carlos Acosta; Robert Hyde; Eva Havrdova; Dana Horakova; Maria Trojano; Giovanna De Luca; Alessandra Lugaresi; Guillermo Izquierdo; Pierre Grammond; Pierre Duquette; Raed Alroughani; Eugenio Pucci; Franco Granella; Jeannette Lechner-Scott; Patrizia Sola; Diana Ferraro; Francois Grand'Maison; Murat Terzi; Csilla Rozsa; Cavit Boz; Raymond Hupperts; Vincent Van Pesch; Celia Oreja-Guevara; Anneke van der Walt; Vilija G Jokubaitis; Tomas Kalincik; Helmut Butzkueven Journal: Pharmacoeconomics Date: 2021-12-18 Impact factor: 4.981
Authors: Yazed AlRuthia; Bander Balkhi; Sahar Abdullah Alkhalifah; Salman Aljarallah; Lama Almutairi; Miteb Alanazi; Abdulmalik Alajlan; Suliman M Aldhafiri; Nuha M Alkhawajah Journal: Int J Environ Res Public Health Date: 2021-12-16 Impact factor: 3.390