Benefit-risk Assessment of Cladribine Using Multi-criteria Decision Analysis (MCDA) for Patients With Relapsing-remitting Multiple Sclerosis.


Journal

Clinical therapeutics
ISSN: 1879-114X
Titre abrégé: Clin Ther
Pays: United States
ID NLM: 7706726

Informations de publication

Date de publication:
02 2019
Historique:
received: 27 07 2018
revised: 03 12 2018
accepted: 28 12 2018
entrez: 9 3 2019
pubmed: 9 3 2019
medline: 12 2 2020
Statut: ppublish

Résumé

We applied Multi-Criteria Decision Analysis (MCDA) methods in a structured benefit-risk assessment of cladribine and newer approved disease-modifying drugs (DMDs) for patients with relapsing-remitting multiple sclerosis (RRMS). Decision conferencing with clinical neurologists as decision makers was used to create an MCDA model that incorporated available evidence on DMDs for RRMS and clinical judgments about the relevance of the evidence. Benefit-risk assessments were conducted for DMDs in both patients with RRMS and patients with RRMS with high disease activity (HDA; defined as ≥2 relapses in the previous year). Treatment options included cladribine and recently approved DMDs available in European Union countries at the time of assessment (December 2015): alemtuzumab, dimethyl fumarate, fingolimod, natalizumab, and teriflunomide. To account for the relative importance of DMD effects, scores for the MCDA model were weighted to ensure that the most clinically important attributes carried more weight in the final benefit-risk calculation. The neurologists weighted different efficacy and safety profile attributes without any reference to individual DMDs to disassociate the assessment of weights with any specific DMD. The neurologists did not do direct comparisons between DMDs. The highest overall weighted preference value for the RRMS model was for dimethyl fumarate (63) followed closely by cladribine (62). For patients with RRMS and HDA, cladribine had the highest overall weighted preference value (76), followed by alemtuzumab (62) and natalizumab (61). The benefit-risk balance of cladribine in patients with RRMS and specifically patients with RRMS who exhibited HDA characterized by high relapse activity (≥2 relapses in the previous year) was more favorable than the other DMDs included in the model. The balance of high efficacy and the safety profile makes cladribine an important treatment option to consider, both in patients with RRMS and patients with HDA. Regular, single-country meetings could be organized to explore how differences in cultural values (scores and weights) and updated input data might affect the usefulness of MCDA in different, real-world, dynamic clinical settings.

Identifiants

pubmed: 30846120
pii: S0149-2918(19)30002-5
doi: 10.1016/j.clinthera.2018.12.015
pii:
doi:

Substances chimiques

Crotonates 0
Hydroxybutyrates 0
Immunosuppressive Agents 0
Natalizumab 0
Nitriles 0
Toluidines 0
teriflunomide 1C058IKG3B
Alemtuzumab 3A189DH42V
Cladribine 47M74X9YT5
Dimethyl Fumarate FO2303MNI2
Fingolimod Hydrochloride G926EC510T

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

249-260.e18

Informations de copyright

Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.

Auteurs

Patrick Vermersch (P)

Department of Neurology, CHU Lille, LIRIC-INSERM U995, FHU Imminent, University of Lille, Lille, France.

Vittorio Martinelli (V)

Neurological Division, San Raffaele Hospital, Milan, Italy.

Claudia Pfleger (C)

Department of Neurology, Aalborg Universitets Hospital, Aalborg, Denmark.

Peter Rieckmann (P)

Department of Neurology, Hospital for Nervous Diseases Medical Park Loipl, Loipl-Bischofswiesen, Germany; Department of Neurology, University of Erlangen, Erlangen, Germany.

Lucia Alonso-Magdalena (L)

Neurology Department, Skåne University Hospital, Malmö, Sweden.

Andrew Galazka (A)

Global Medical Excellence, Merck, Aubonne, Switzerland.

Fernando Dangond (F)

Global Clinical Development, Neurology, EMD Serono, Inc, Billerica, Massachusetts, USA.

Lawrence Phillips (L)

Department of Management, London School of Economics and Political Science, London, United Kingdom. Electronic address: larry_phillips@msn.com.

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Classifications MeSH