Patient and neurologist preferences in the UK for relapsing–remitting multiple sclerosis treatments: findings from a discrete choice experiment

References

• Lee J, Dunn J. Mobility concerns in multiple sclerosis—studies and surveys on US patient populations of relevance to nurses. US Neurol. 2013;9(1):17.
   Google Scholar
• MSTrust. Prevalence and incidence of multiple sclerosis 2020 [Internet] [cited 2020 Nov 11]. Available from: https://www.mstrust.org.uk/a-z/prevalence-and-incidence-multiple-sclerosis
   Google Scholar
• Antao L, Shaw L, Ollson K, et al. Chronic pain in episodic illness and its influence on work occupations: a scoping review. Work. 2013;44(1):11–36.
   PubMedGoogle Scholar
• Schapiro R. Managing the symptoms of multiple sclerosis. 5th ed. New York: Demos Medical Publishing; 2007.
   Google Scholar
• Hooper K. Managing progressive MS [Internet]. National MS Society; 2018 [cited 2020 Nov 11]. Available from: https://www.nationalmssociety.org/NationalMSSociety/media/MSNationalFiles/Brochures/Brochure_Managing_Progressive_MS.pdf
   Google Scholar
• Rae-Grant A, Day GS, Marrie RA, et al. Practice guideline recommendations summary: disease-modifying therapies for adults with multiple sclerosis: report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. Neurology. 2018;90(17):777–788.
   PubMed Web of Science ®Google Scholar
• Tran JQ, Hartung JP, Peach RJ, et al. Results from the first-in-human study with ozanimod, a novel, selective sphingosine-1-phosphate receptor modulator. J Clin Pharmacol. 2017;57(8):988–996.
   PubMed Web of Science ®Google Scholar
• Bermel RA, Bakshi R. The measurement and clinical relevance of brain atrophy in multiple sclerosis. Lancet Neurol. 2006;5(2):158–170.
   PubMed Web of Science ®Google Scholar
• Chard DT, Griffin CM, Parker GJ, et al. Brain atrophy in clinically early relapsing–remitting multiple sclerosis. Brain. 2002;125(Pt 2):327–337.
   PubMedGoogle Scholar
• De Stefano N, Matthews PM, Filippi M, et al. Evidence of early cortical atrophy in MS: relevance to white matter changes and disability. Neurology. 2003;60(7):1157–1162.
   PubMed Web of Science ®Google Scholar
• Eshaghi A, Prados F, Brownlee WJ, et al. Deep gray matter volume loss drives disability worsening in multiple sclerosis. Ann Neurol. 2018;83(2):210–222.
   PubMed Web of Science ®Google Scholar
• Radue EW, Barkhof F, Kappos L, et al. Correlation between brain volume loss and clinical and MRI outcomes in multiple sclerosis. Neurology. 2015;84(8):784–793.
   PubMed Web of Science ®Google Scholar
• Roosendaal SD, Bendfeldt K, Vrenken H, et al. Grey matter volume in a large cohort of MS patients: relation to MRI parameters and disability. Mult Scler. 2011;17(9):1098–1106.
   PubMed Web of Science ®Google Scholar
• Sanfilipo MP, Benedict RH, Sharma J, et al. The relationship between whole brain volume and disability in multiple sclerosis: a comparison of normalized gray vs. white matter with misclassification correction. Neuroimage. 2005;26(4):1068–1077.
   PubMed Web of Science ®Google Scholar
• Moccia M, Quarantelli M, Lanzillo R, et al. Grey:white matter ratio at diagnosis and the risk of 10-year multiple sclerosis progression. Eur J Neurol. 2017;24(1):195–204.
   PubMed Web of Science ®Google Scholar
• Siffrin V, Vogt J, Radbruch H, et al. Multiple sclerosis – candidate mechanisms underlying CNS atrophy. Trends Neurosci. 2010;33(4):202–210.
   PubMed Web of Science ®Google Scholar
• Horakova D, Kalincik T, Dusankova JB, et al. Clinical correlates of grey matter pathology in multiple sclerosis. BMC Neurol. 2012;12:10.
   PubMed Web of Science ®Google Scholar
• Minagar A, Barnett MH, Benedict RH, et al. The thalamus and multiple sclerosis: modern views on pathologic, imaging, and clinical aspects. Neurology. 2013;80(2):210–219.
   PubMed Web of Science ®Google Scholar
• Popescu BFG, Lucchinetti CF. Meningeal and cortical grey matter pathology in multiple sclerosis. BMC Neurol. 2012;12:11.
   PubMed Web of Science ®Google Scholar
• Gold R, Wolinsky JS, Amato MP, et al. Evolving expectations around early management of multiple sclerosis. Ther Adv Neurol Disord. 2010;3(6):351–367.
   PubMedGoogle Scholar
• Jacques FH. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology. 2015;84(9):963.
   PubMed Web of Science ®Google Scholar
• Tedeholm H, Lycke J, Skoog B, et al. Time to secondary progression in patients with multiple sclerosis who were treated with first generation immunomodulating drugs. Mult Scler. 2013;19(6):765–774.
   PubMed Web of Science ®Google Scholar
• Cohen JA, Comi G, Selmaj KW, et al. Ozanimod vs interferon β-1a: clinical and MRI results of RADIANCE part B – a 2-year phase 3 trial in relapsing multiple sclerosis [abstract 280]. Mult Scler J. 2017;23(3 Suppl):981–982.
   Google Scholar
• De Stefano N, Silva DG, Barnett MH. Effect of fingolimod on brain volume loss in patients with multiple sclerosis. CNS Drugs. 2017;31(4):289–305.
   PubMed Web of Science ®Google Scholar
• Khan O, Bao F, Shah M, et al. Effect of disease-modifying therapies on brain volume in relapsing–remitting multiple sclerosis: results of a five-year brain MRI study. J Neurol Sci. 2012;312(1–2):7–12.
   PubMed Web of Science ®Google Scholar
• Zivadinov R, Dwyer MG, Carl E, et al. Evaluating the effect of teriflunomide on whole brain atrophy in the phase 3 TOPIC study [abstract p870]. Presented at 34th Congress of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS); 2018 Oct 10–12; Berlin, Germany.
   Google Scholar
• Tullman MJ. A review of current and emerging therapeutic strategies in multiple sclerosis. Am J Manag Care. 2013;19(2 Suppl):S21–S27.
   PubMed Web of Science ®Google Scholar
• Jonker MF, Donkers B, Goossens LMA, et al. Summarizing patient preferences for the competitive landscape of multiple sclerosis treatment options. Med Decis Making. 2020;40(2):198–211.
   PubMed Web of Science ®Google Scholar
• Visser LA, Louapre C, Uyl-de Groot CA, et al. Patient needs and preferences in relapsing–remitting multiple sclerosis: a systematic review. Mult Scler Relat Disord. 2020;39:101929.
   PubMed Web of Science ®Google Scholar
• Webb EJD, Meads D, Eskyte I, et al. A systematic review of discrete-choice experiments and conjoint analysis studies in people with multiple sclerosis. Patient. 2018;11(4):391–402.
   PubMedGoogle Scholar
• Mansfield C, Thomas N, Gebben D, et al. Preferences for multiple sclerosis treatments: using a discrete-choice experiment to examine differences across subgroups of US patients. Int J MS Care. 2017;19(4):172–183.
   PubMedGoogle Scholar
• Poulos C, Wakeford C, Kinter E, et al. Patient and physician preferences for multiple sclerosis treatments in Germany: a discrete-choice experiment study. Mult Scler J Exp Transl Clin. 2020;6(1):2055217320910778.
   Google Scholar
• Johnson FR, Lancsar E, Marshall D, et al. Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force. Value Health. 2013;16(1):3–13.
   PubMed Web of Science ®Google Scholar
• Tencer T, Will O, Nguyen J, et al. Neurologist and patient preferences in multiple sclerosis: UK and US qualitative research findings [abstract PND107]. Value Health. 2019;22(3):S757.
   Google Scholar
• Biogen. Avonex (interferon beta-1a) injection [package insert]. Cambridge (MA): Biogen; 2019.
   Google Scholar
• Genentech. Ocrevus (ocrelizumab) injection [package insert]. South San Francisco (CA): Genentech; 2017.
   Google Scholar
• Novartis. Efficacy and safety of fingolimod in patients with relapsing–remitting multiple sclerosis (FREEDOMS). NLM identifier: NCT00289978; 2012.
   Google Scholar
• Serono EMD. Rebif (interferon beta-1a) [package insert]. Rockland (MA): Serono EMD; 2019.
   Google Scholar
• Teva. Copaxone (glatiramer acetate injection) [package insert]. Parsippany (NJ): Teva; 2019.
   Google Scholar
• Vermersch P, Martinelli V, Pfleger C, et al. Benefit–risk assessment of cladribine using multi-criteria decision analysis (MCDA) for patients with relapsing–remitting multiple sclerosis. Clin Ther. 2019;41(2):249–260.e18.
   PubMed Web of Science ®Google Scholar
• Schwartz CE, Coulthard-Morris L, Zeng Q, et al. Measuring self-efficacy in people with multiple sclerosis: a validation study. Arch Phys Med Rehabil. 1996;77(4):398–398.
   Web of Science ®Google Scholar
• Chiu CY, Motl RW. Further validation of the Multiple Sclerosis Self-Efficacy Scale. Disabil Rehabil. 2015;37(26):2429–2438.
   PubMed Web of Science ®Google Scholar
• Vickrey BG, Hays RD, Harooni R, et al. A health-related quality of life measure for multiple sclerosis. Qual Life Res. 1995;4(3):187–206.
   PubMed Web of Science ®Google Scholar
• Silveira C, Guedes R, Maia D, et al. Neuropsychiatric symptoms of multiple sclerosis: state of the art. Psychiatry Investig. 2019;16(12):877–888.
   PubMed Web of Science ®Google Scholar
• Azevedo CJ, Pelletier D. Whole-brain atrophy: ready for implementation into clinical decision-making in multiple sclerosis? Curr Opin Neurol. 2016;29(3):237–242.
   PubMed Web of Science ®Google Scholar
• Nowinski CJ, Miller DM, Cella D. Evolution of patient-reported outcomes and their role in multiple sclerosis clinical trials. Neurotherapeutics. 2017;14(4):934–944.
   PubMed Web of Science ®Google Scholar
• Moccia M, Valsecchi N, Ciccarelli O, et al. Spinal cord atrophy in a primary progressive multiple sclerosis trial: improved sample size using GBSI. Neuroimage Clin. 2020;28:102418.
   PubMed Web of Science ®Google Scholar
• Kantar Health. Epi Database, United Kingdom 2020 [Internet]. New York: Kantar Health; 2010 [updated 2020 Jun 23; cited 2021 Jun 2]. Available from: www.epidb.com
   Google Scholar