OBJECTIVE: The cost-effectiveness of new oral disease-modifying therapies (DMTs) has not been modeled in highly active (HA) relapsing-remitting multiple sclerosis (RRMS) requiring escalation therapy. This study sought to model the cost-effectiveness of fingolimod compared to dimethyl fumarate (DMF), for which relevant HA RRMS sub-group data were available, from the perspective of the National Health Service (NHS) in England. METHODS: A cohort Markov model based on Expanded Disability Status Scale scores, similar to previous model designs, was constructed. Published post hoc clinical data in the HA RRMS sub-groups were taken from the pivotal trials for fingolimod and DMF vs placebo. Utility data for each health state and for relapses were used in line with previous similar models. Published costs were inflated to NHS cost year 2013-2014 and UK list prices used for both drugs. Possible Patient Access Scheme (PAS) discount scenarios were investigated. RESULTS: In the base case, using list prices for each DMT, the average probabilistic incremental cost-effectiveness ratio (ICER) for fingolimod vs DMF was found to be £ 14,076, with a 73% chance of fingolimod being cost-effective at a willingness-to-pay threshold of £ 30,000. Scenario and sensitivity analyses showed that uncertainty in disability progression efficacy was a key model driver. The model was robust to other changes and the majority of PAS permutations do not contradict the base case finding of cost-effectiveness of fingolimod. CONCLUSIONS: In conclusion, fingolimod remains cost-effective in HA RRMS following the introduction of DMF to the UK market, and this paper supports the evidence that has led fingolimod to be the only oral DMT reimbursed for HA RRMS in England. This model supports the restriction imposed by National Institute for Health and Care Excellence (NICE) on DMF in HA RRMS and highlights the importance of considering different sub-groups of multiple sclerosis when performing health economic analyses.
OBJECTIVE: The cost-effectiveness of new oral disease-modifying therapies (DMTs) has not been modeled in highly active (HA) relapsing-remitting multiple sclerosis (RRMS) requiring escalation therapy. This study sought to model the cost-effectiveness of fingolimod compared to dimethyl fumarate (DMF), for which relevant HA RRMS sub-group data were available, from the perspective of the National Health Service (NHS) in England. METHODS: A cohort Markov model based on Expanded Disability Status Scale scores, similar to previous model designs, was constructed. Published post hoc clinical data in the HA RRMS sub-groups were taken from the pivotal trials for fingolimod and DMF vs placebo. Utility data for each health state and for relapses were used in line with previous similar models. Published costs were inflated to NHS cost year 2013-2014 and UK list prices used for both drugs. Possible Patient Access Scheme (PAS) discount scenarios were investigated. RESULTS: In the base case, using list prices for each DMT, the average probabilistic incremental cost-effectiveness ratio (ICER) for fingolimod vs DMF was found to be £ 14,076, with a 73% chance of fingolimod being cost-effective at a willingness-to-pay threshold of £ 30,000. Scenario and sensitivity analyses showed that uncertainty in disability progression efficacy was a key model driver. The model was robust to other changes and the majority of PAS permutations do not contradict the base case finding of cost-effectiveness of fingolimod. CONCLUSIONS: In conclusion, fingolimod remains cost-effective in HA RRMS following the introduction of DMF to the UK market, and this paper supports the evidence that has led fingolimod to be the only oral DMT reimbursed for HA RRMS in England. This model supports the restriction imposed by National Institute for Health and Care Excellence (NICE) on DMF in HA RRMS and highlights the importance of considering different sub-groups of multiple sclerosis when performing health economic analyses.
Authors: Philippe Laramée; Aurélie Millier; Thor-Henrik Brodtkorb; Nora Rahhali; Olivier Cristeau; Samuel Aballéa; Stephen Montgomery; Sara Steeves; Mondher Toumi; Jürgen Rehm Journal: Clin Drug Investig Date: 2016-11 Impact factor: 2.859
Authors: Marita Zimmermann; Elizabeth Brouwer; Jeffrey A Tice; Matt Seidner; Anne M Loos; Shanshan Liu; Richard H Chapman; Varun Kumar; Josh J Carlson Journal: CNS Drugs Date: 2018-12 Impact factor: 5.749
Authors: Eline Huisman; Katerina Papadimitropoulou; James Jarrett; Matthew Bending; Zoe Firth; Felicity Allen; Nick Adlard Journal: BMJ Open Date: 2017-03-10 Impact factor: 2.692
Authors: Ingrid E H Kremer; Mickael Hiligsmann; Josh Carlson; Marita Zimmermann; Peter J Jongen; Silvia M A A Evers; Svenja Petersohn; Xavier G L V Pouwels; Nick Bansback Journal: Med Decis Making Date: 2020-11 Impact factor: 2.583