TY - JOUR
T1 - Head-to-head drug comparisons in multiple sclerosis
T2 - Urgent action needed
AU - Tur, Carmen
AU - Kalincik, Tomas
AU - Oh, Jiwon
AU - Sormani, Maria P.
AU - Tintoré, Mar
AU - Butzkueven, Helmut
AU - Montalban, Xavier
N1 - Funding Information:
C. Tur has received a postdoctoral research ECTRIMS fellowship. She has also received honoraria and support for traveling from Merck Serono, Sanofi, Roche, Teva Pharmaceuticals, Novartis, Biogen, Bayer, and Ismar Healthcare. She also provides consultancy services to Roche. T. Kalincik reports grants from the National Health and Medical Research Council (Australia) and Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne; grants, personal fees, and nonfinancial support from Biogen; personal fees from Roche, Teva, and BioCSL; personal fees and nonfinancial support from Sanofi Genzyme, Merck, and Novartis; and personal fees from WebMD Global. J. Oh receives grant support from The MS Society of Canada, the National MS Society, Brain Canada, Biogen Idec, and Roche. She has also received personal compensation for consulting or speaking from EMD Serono, Genzyme, Biogen Idec, Novartis, Celgene, and Roche. M.P. Sormani has received consulting fees from Biogen, GeNeuro, Genzyme, Merck Serono, Novartis, Roche, Teva Pharmaceuticals, and Vertex. M. Tintoréhas received compensation for consulting services and speaking honoraria from Almirall, Bayer Schering Pharma, Biogen Idec, Genzyme, Merck Serono, Novartis, Roche, Sanofi-Aventis, and Teva Pharmaceuticals. MT is coeditor of Multiple Sclerosis Journal-ETC. H. Butzkueven reports grant and research support from the NHMRC, MS Research Australia, Monash University, Novartis, Roche, and the Pennycook Foundation. He reports honoraria and travel expenses for participation in scientific meetings, steering committees, and advisory boards from Novartis, Biogen, Merck, Roche, Genzyme, and Oxford Health Policy Forum. X. Montalban has received speaking honoraria and travel expenses for participation in scientific meetings, has been a steering committee member of clinical trials, or participated in advisory boards of clinical trials in the past with Actelion, Amirall, Bayer, Biogen, Celgene, Genzyme, Hoffmann-La Roche, Novartis, Oryzon Genomics, Sanofi Genzyme, and Teva Pharmaceutical. Go to Neurology.org/ N for full disclosures.
Funding Information:
Abbreviations: ALZ = alemtuzumab; CLA = cladribine; DMF = dimethyl fumarate; FTY = fingolimod; GA = glatiramer acetate; IFNb-1a/1b = interferon beta-1a/1b; IM = intramuscular; IQR = interquartile range; MS = multiple sclerosis; NA = not available; NEDA = no evidence of disease activity; NTZ = natalizumab; OFSEP = Observatoire Français de la Sclérose en Plaques; PO = per oral; RRMS = relapsing-remitting MS; RTX = rituximab; SC = subcutaneous; SPMS = secondary progressive MS; TER = teriflunomide; TOP = Tysabri Observational Study. >: superior to; 1: when the measure of dispersion provided is SD, the mean is usually reported as the average measure; when the measure of dispersion provided is range or interquartile range, the median is instead reported as the average measure; 2: including sources of bias in addition to indication bias; 3: high percentage of missing data on MRI; 4: unless otherwise specified, “Disability” here refers to disability accumulation; 5: this group includes SC IFNb-1a, SC IFNb-1b, IM IFNb-1a, and SC GA; 6: study population was stratified according to baseline MRI and previous history of relapses; 7: MRI outcomes were only reported in this study (Alping et al., Ann Neurol 2016); 8: Poisson models were fitted. Times of drug exposure are presented as person-years, but no information on the dispersion of exposure in the population was reported; 9: only patients with at least 6-month follow-up periods were included. Cox marginal models were used, and only a 6-month follow-up period was considered; 10: weighted median across groups (extracted from article); 11: all patients had to have at least 1 year of follow-up on monotherapy, and outcomes were all related to this 1-year follow-up period; 12: the main results refer to a whole-cohort analysis, including patients with RRMS and SPMS; however, a subgroup analysis taking into account only RRMS showed similar results; 13: the MSBase receives support from Merck, Biogen, Novartis, Roche, Bayer Schering, Sanofi Genzyme, and Teva; 14: the Italian iMed-Web database receives support from the Italian University and Research Ministry (MIUR) and the pharma companies Merck Serono, Novartis Pharma, and Biogen; 15: the OFSEP database is supported by a grant provided by the French State and handled by the Agence Nationale de la Recherche. E-references are available on Dryad, doi.org/10.5061/dryad.h5g7nt7.
Publisher Copyright:
Copyright © 2019 American Academy of Neurology.
PY - 2019/10/29
Y1 - 2019/10/29
N2 - Disease-modifying drugs are changing the natural history of multiple sclerosis (MS). However, currently available clinical trial data are insufficient to develop accurate personalized treatment algorithms to assign the best possible treatment to each person with MS according to disease features, treatment history, and comorbidities. Such accurate algorithms would require the presence of numerous head-to-head trials of long duration, which is virtually impossible, given the economic costs, required time, and difficulties with attrition. Thus, efforts are being made to compare relative treatment efficacy through observational designs, using large multicenter prospective cohorts or "big MS data," and network meta-analyses. Although such studies can yield useful information, they are liable to biases and their results should be confirmed in other study populations, including smaller, single-center cohorts, where some of these biases can be minimized. In this View article, we analyze the potential benefits and biases of all these strategies alternative to head-to-head trials in MS. Finally, we propose the combination of all these types of studies to obtain reliable head-to-head drug comparisons in the absence of randomized designs.
AB - Disease-modifying drugs are changing the natural history of multiple sclerosis (MS). However, currently available clinical trial data are insufficient to develop accurate personalized treatment algorithms to assign the best possible treatment to each person with MS according to disease features, treatment history, and comorbidities. Such accurate algorithms would require the presence of numerous head-to-head trials of long duration, which is virtually impossible, given the economic costs, required time, and difficulties with attrition. Thus, efforts are being made to compare relative treatment efficacy through observational designs, using large multicenter prospective cohorts or "big MS data," and network meta-analyses. Although such studies can yield useful information, they are liable to biases and their results should be confirmed in other study populations, including smaller, single-center cohorts, where some of these biases can be minimized. In this View article, we analyze the potential benefits and biases of all these strategies alternative to head-to-head trials in MS. Finally, we propose the combination of all these types of studies to obtain reliable head-to-head drug comparisons in the absence of randomized designs.
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U2 - 10.1212/WNL.0000000000008319
DO - 10.1212/WNL.0000000000008319
M3 - Review article
C2 - 31591277
AN - SCOPUS:85074244977
SN - 0028-3878
VL - 93
SP - 793
EP - 809
JO - Neurology
JF - Neurology
IS - 18
ER -