Diogo Mendes1,2, Carlos Alves3,4, Francisco Batel-Marques3,4. 1. AIBILI-Association for Innovation and Biomedical Research on Light and Image, CHAD-Centre for Health Technology Assessment and Drug Research, Azinhaga de Santa Comba, Celas, 3000-548, Coimbra, Portugal. diogomendes26@gmail.com. 2. School of Pharmacy, University of Coimbra, Coimbra, Portugal. diogomendes26@gmail.com. 3. AIBILI-Association for Innovation and Biomedical Research on Light and Image, CHAD-Centre for Health Technology Assessment and Drug Research, Azinhaga de Santa Comba, Celas, 3000-548, Coimbra, Portugal. 4. School of Pharmacy, University of Coimbra, Coimbra, Portugal.
Abstract
OBJECTIVE: This study aimed to test the number needed to treat to benefit (NNTB) and to harm (NNTH), and the likelihood to be helped or harmed (LHH) when assessing benefits, risks, and benefit-risk ratios of disease-modifying treatments (DMTs) approved for relapsing-remitting multiple sclerosis (RRMS). METHODS: In May 2016, we conducted a systematic review using the PubMed and Cochrane Central Register of Controlled Trials databases to identify phase III, randomized controlled trials with a duration of ≥2 years that assessed first-line (dimethyl fumarate [DMF], glatiramer acetate [GA], β-interferons [IFN], and teriflunomide) or second-line (alemtuzumab, fingolimod, and natalizumab) DMTs in patients with RRMS. Meta-analyses were performed to estimate relative risks (RRs) on annualized relapse rate (ARR), proportion of relapse-free patients (PPR-F), disability progression (PP-F-CDPS3M), and safety outcomes. NNTB and NNTH values were calculated applying RRs to control event rates. LHH was calculated as NNTH/NNTB ratio. RESULTS: The lowest NNTBs on ARR, PPR-F, and PP-F-CDPS3M were found with IFN-β-1a-SC (NNTB 3, 95 % CI 2-4; NNTB 7, 95 % CI 4-18; NNTB 4, 95 % CI 3-7, respectively) and natalizumab (NNTB 2, 95 % CI 2-3; NNTB 4, 95 % CI 3-6; NNTB 9, 95 % CI 6-19, respectively). The lowest NNTH on adverse events leading to treatment discontinuation was found with IFN-β-1b (NNTH 14, 95 % 2-426) versus placebo; a protective effect was noted with alemtuzumab versus IFN-β-1a-SC (NNTB 22, 95 % 17-41). LHHs >1 were more frequent with IFN-β-1a-SC and natalizumab. CONCLUSIONS: These metrics may be valuable for benefit-risk assessments, as they reflect baseline risks and are easily interpreted. Before making treatment decisions, clinicians must acknowledge that a higher RR reduction with drug A as compared with drug B (versus a common comparator in trial A and trial B, respectively) does not necessarily mean that the number of patients needed to be treated for one patient to encounter one aditional outcome of interest over a defined period of time is lower with drug A than with drug B. Overall, IFN-β-1a-SC and natalizumab seem to have the most favorable benefit-risk ratios among first- and second-line DMTs, respectively.
OBJECTIVE: This study aimed to test the number needed to treat to benefit (NNTB) and to harm (NNTH), and the likelihood to be helped or harmed (LHH) when assessing benefits, risks, and benefit-risk ratios of disease-modifying treatments (DMTs) approved for relapsing-remitting multiple sclerosis (RRMS). METHODS: In May 2016, we conducted a systematic review using the PubMed and Cochrane Central Register of Controlled Trials databases to identify phase III, randomized controlled trials with a duration of ≥2 years that assessed first-line (dimethyl fumarate [DMF], glatiramer acetate [GA], β-interferons [IFN], and teriflunomide) or second-line (alemtuzumab, fingolimod, and natalizumab) DMTs in patients with RRMS. Meta-analyses were performed to estimate relative risks (RRs) on annualized relapse rate (ARR), proportion of relapse-free patients (PPR-F), disability progression (PP-F-CDPS3M), and safety outcomes. NNTB and NNTH values were calculated applying RRs to control event rates. LHH was calculated as NNTH/NNTB ratio. RESULTS: The lowest NNTBs on ARR, PPR-F, and PP-F-CDPS3M were found with IFN-β-1a-SC (NNTB 3, 95 % CI 2-4; NNTB 7, 95 % CI 4-18; NNTB 4, 95 % CI 3-7, respectively) and natalizumab (NNTB 2, 95 % CI 2-3; NNTB 4, 95 % CI 3-6; NNTB 9, 95 % CI 6-19, respectively). The lowest NNTH on adverse events leading to treatment discontinuation was found with IFN-β-1b (NNTH 14, 95 % 2-426) versus placebo; a protective effect was noted with alemtuzumab versus IFN-β-1a-SC (NNTB 22, 95 % 17-41). LHHs >1 were more frequent with IFN-β-1a-SC and natalizumab. CONCLUSIONS: These metrics may be valuable for benefit-risk assessments, as they reflect baseline risks and are easily interpreted. Before making treatment decisions, clinicians must acknowledge that a higher RR reduction with drug A as compared with drug B (versus a common comparator in trial A and trial B, respectively) does not necessarily mean that the number of patients needed to be treated for one patient to encounter one aditional outcome of interest over a defined period of time is lower with drug A than with drug B. Overall, IFN-β-1a-SC and natalizumab seem to have the most favorable benefit-risk ratios among first- and second-line DMTs, respectively.
Authors: G M Hadjigeorgiou; C Doxani; M Miligkos; P Ziakas; G Bakalos; D Papadimitriou; T Mprotsis; N Grigoriadis; E Zintzaras Journal: J Clin Pharm Ther Date: 2013-08-20 Impact factor: 2.512
Authors: Peter A Calabresi; Ernst-Wilhelm Radue; Douglas Goodin; Douglas Jeffery; Kottil W Rammohan; Anthony T Reder; Timothy Vollmer; Mark A Agius; Ludwig Kappos; Tracy Stites; Bingbing Li; Linda Cappiello; Philipp von Rosenstiel; Fred D Lublin Journal: Lancet Neurol Date: 2014-03-28 Impact factor: 44.182
Authors: Damiano Paolicelli; Giuseppe Lucisano; Alessia Manni; Carlo Avolio; Simona Bonavita; Vincenzo Brescia Morra; Marco Capobianco; Eleonora Cocco; Antonella Conte; Giovanna De Luca; Francesca De Robertis; Claudio Gasperini; Maurizia Gatto; Paola Gazzola; Giacomo Lus; Antonio Iaffaldano; Pietro Iaffaldano; Davide Maimone; Giulia Mallucci; Giorgia T Maniscalco; Girolama A Marfia; Francesco Patti; Ilaria Pesci; Carlo Pozzilli; Marco Rovaris; Giuseppe Salemi; Marco Salvetti; Daniele Spitaleri; Rocco Totaro; Mauro Zaffaroni; Giancarlo Comi; Maria Pia Amato; Maria Trojano Journal: J Neurol Date: 2019-09-18 Impact factor: 4.849
Authors: Marcello Moccia; Ilaria Loperto; Roberta Lanzillo; Antonio Capacchione; Antonio Carotenuto; Maria Triassi; Vincenzo Brescia Morra; Raffaele Palladino Journal: BMC Health Serv Res Date: 2020-08-26 Impact factor: 2.655