Hostname: page-component-848d4c4894-mwx4w Total loading time: 0 Render date: 2024-06-18T16:45:26.841Z Has data issue: false hasContentIssue false

Desirability and acceptability of a treatment-sequencing model in relapsing-remitting multiple sclerosis: A health technology assessment perspective

Published online by Cambridge University Press:  19 May 2020

Marjanne A. Piena
Affiliation:
Pharmerit International, Rotterdam, The Netherlands
Olaf Schoeman
Affiliation:
Pharmerit International, Berlin, Germany
Gerard T. Harty
Affiliation:
EMD Serono, Inc., Billerica, MA, USA
Schiffon L. Wong
Affiliation:
EMD Serono, Inc., Billerica, MA, USA

Abstract

Objective

Gather health technology assessment (HTA) experts' insights on the desirability and acceptability of treatment-sequencing models applied to relapsing-remitting multiple sclerosis (RRMS).

Data source/study setting

Primary data.

Study design

In-depth double-blind semi-structured telephone interviews.

Data collection/extraction methods

General themes were extracted from qualitative interviews.

Principal findings

Although experts confirmed the importance of evaluating the clinical and cost-effectiveness of treatments as part of a sequence, the current HTA decision making framework is not conducive to this. Developing an RRMS treatment-sequencing model that meets HTA requirements is difficult, in particular due to scarcity of effectiveness data in later treatment lines.

Conclusions

At present, a treatment-sequencing model for RRMS may be desirable yet not requested by HTA bodies for their decision making. However, there could be other areas where a treatment-sequencing model for RRMS is of use.

Type
Assessment
Copyright
Copyright © Cambridge University Press 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Loma, I, Heyman, R. Multiple sclerosis: pathogenesis and treatment. Curr Neuropharmacol. 2011;9:409416.CrossRefGoogle ScholarPubMed
Losy, J. Is MS an inflammatory or primary degenerative disease? J Neural Transm. 2013;120:1459–62.CrossRefGoogle ScholarPubMed
Lublin, FD, Reingold, SC, Cohen, JA, Cutter, GR, Sørensen, PS, Thompson, AJ, et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology. 2014;83:278–86.CrossRefGoogle ScholarPubMed
Farber, RS, Sand, IK. Optimizing the initial choice and timing of therapy in relapsing-remitting multiple sclerosis. Ther Adv Neurol Disord. 2015;8:212–32.CrossRefGoogle ScholarPubMed
Giovannoni, G, Butzkueven, H, Dhib-Jalbut, S, Hobart, J, Kobelt, G, Pepper, G, et al. Brain health: time matters in multiple sclerosis. Mult Scler Relat Disord. 2016;9:S5S48.CrossRefGoogle Scholar
Johnson, KM, Zhou, H, Lin, F, Ko, JJ, Herrera, V. Real-world adherence and persistence to oral disease-modifying therapies in multiple sclerosis patients over 1 year. J Manag Care Spec Pharm. 2017;23:844–52.Google ScholarPubMed
Grand'Maison, F, Yeung, M, Morrow, SA, Lee, L, Emond, F, Ward, BJ, et al. Sequencing of disease-modifying therapies for relapsing-remitting multiple sclerosis: a theoretical approach to optimizing treatment. Curr Med Res Opin. 2018;34:14191430.CrossRefGoogle ScholarPubMed
Merkel, B, Butzkueven, H, Traboulsee, AL, Havrdova, E, Kalincik, T. Timing of high-efficacy therapy in relapsing-remitting multiple sclerosis: A systematic review. Autoimmun Rev. 2017;16:658–65.CrossRefGoogle ScholarPubMed
Menzin, J, Caon, C, Nichols, C, White, LA, Friedman, M, Pill, MW. Narrative review of the literature on adherence to disease-modifying therapies among patients with multiple sclerosis. J Manag Care Pharm. 2013;19:S2440.Google ScholarPubMed
Allen, F, Montgomery, S, Maruszczak, M, Kusel, J, Adlard, N. Convergence yet continued complexity: A systematic review and critique of health economic models of relapsing-remitting multiple sclerosis in the United Kingdom. Value Health. 2015;18:925938.CrossRefGoogle ScholarPubMed
Hernandez, L, O'Donnell, M, Postma, M. Modeling approaches in cost-effectiveness analysis of disease-modifying therapies for relapsing-remitting multiple sclerosis: An updated systematic review and recommendations for future economic evaluations. Pharmacoeconomics. 2018;36:12231252.CrossRefGoogle ScholarPubMed
National Institute for Health and Care Excellence. TA247: Tocilizumab for the treatment of rheumatoid arthritis. London, UK: National Institute for Health and Care Excellence; 2012.Google Scholar
Brennan, A, Bansback, N, Reynolds, A, Conway, P. Modelling the cost-effectiveness of etanercept in adults with rheumatoid arthritis in the UK. Rheumatology (Oxford). 2004;43:6272.CrossRefGoogle ScholarPubMed
Tosh, J, Stevenson, M, Akehurst, R. Health economic modelling of treatment sequences for rheumatoid arthritis: a systematic review. Curr Rheumatol Rep. 2014;16:447.CrossRefGoogle ScholarPubMed
National Institute for Health and Care Excellence. TA303: Teriflunomide for treating relapsing–remitting multiple sclerosis. London, UK: National Institute for Health and Care Excellence; 2014.Google Scholar
Supplementary material: PDF

Piena et al. supplementary material

Piena et al. supplementary material

Download Piena et al. supplementary material(PDF)
PDF 193.2 KB