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Comparison of fingolimod, dimethyl fumarate and teriflunomide for multiple sclerosis
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  • Published on:
    Comparison of fingolimod, dimethyl fumarate and teriflunomide for multiple sclerosis: when methodology does not hold the promise
    • Robert W Platt, Professor Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada
    • Other Contributors:
      • Mohammad E Karim, Assistant Professor
      • Thomas PA Debray, Assistant Professor
      • Massimiliano Copetti, Biostatistician, Head
      • Georgios Tsivgoulis, Professor
      • Emmanuelle Waubant, Neurologist
      • Hans P Hartung, Professor

    Dear Editor,

    We read with interest the article by Kalincik et al. [1] comparing fingolimod, dimethyl fumarate and teriflunomide in a cohort of relapsing-remitting multiple sclerosis (MS) patients. The authors investigated several endpoints and performed various sensitivity analyses, and we commend them for reporting technical details in the online supplementary material. We, however, have some concerns about the design, analysis and reporting of the study.

    1. In the primary analyses, three separate propensity score models were developed to construct a matched cohort for each of the three pairwise comparisons. Supplementary Table 6 clearly indicates the existence of zero or low frequencies in some variables (e.g., most active previous therapy and magnetic resonance imaging [MRI] T2 lesions). Yet, those variables were used as covariates in the propensity score models, unsurprisingly resulting in extremely high point estimates and standard errors (SE; as reported in Supplementary Table 7). For example, teriflunomide was not the most active therapy for any patient in the dimethyl fumarate cohort (n=0 from Supplementary Table 6), but that category was nevertheless included in the propensity score model, leading to an unrealistic point estimate of 18.65 with SE of 434.5 (Supplementary Table 7). Even higher SEs (greater than 1000) are observed in the other propensity score models. Propensity scores estimated from these poorly constructed models were then used to cr...

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    Conflict of Interest:
    RW Platt has received consulting fees from Biogen and has participated in advisory boards for Biogen. He has received consulting fees from Amgen, Depuy, Eli Lilly, Merck, and Pfizer for projects unrelated to the current work.
    MEK has received consulting fees from Biogen and participated in advisory Boards and/or Satellite Symposia of Biogen.
    TPA Debray received consulting fees from Biogen and participated in advisory Boards and/or Satellite Symposia of Biogen.
    M Copetti received consulting fees from Biogen, Intercept, Eisai and Teva and participated in advisory Boards and/or Satellite Symposia of Biogen.
    G Tsivgoulis has received travel grant support and/or has participated in advisory Boards and/or Satellite Symposia of Biogen, Merck, Bayer, Teva, Sanofi and Roche
    E Waubant has received honoraria for talks from Medscape and The Corpus. She has funding from PCORI, NMSS, NIH, and the Race to Erase MS. She is site PI for trials with Biogen, Novartis and Roche. She is section editor for Annals in Clinical and Transnational Neurology and co-editor in chief for MS and Related Disorders.
    HP Hartung received honoraria for serving on steering and data monitoring committees, consulting and speaking from BayerHealthcare, Biogen, Geneuro, Merck, Novartis, Receptos Celgene, Roche, Sanofi Genzyme, Teva, TG Therapeutics with approval by the Rector of Heinrich-Heine-University.