BackgroundComparative effectiveness (CE) research allows real-world treatment comparisons using outcome measurements important to physicians/patients. This German NeuroTransData registry-based analysis compared delayed-release dimethyl fumarate (DMF) effectiveness with interferons (IFN), glatiramer acetate (GA), teriflunomide (TERI), or fingolimod (FTY) in patients with relapsing–remitting multiple sclerosis (RRMS) using propensity score matching (PSM).MethodsData from registry patients aged ≥ 18 years with RRMS, ≥ 1 relapse, and Expanded Disability Status Scale (EDSS) assessment(s) after index therapy initiation underwent 1:1 PSM to match DMF with comparator populations baseline characteristics. Primary outcome measurement was time to first relapse (TTFR). Secondary outcome measurements included annualised relapse rate (ARR), proportion of patients relapse free at 12 and 24 months, time to index therapy discontinuation (TTD), and reasons for discontinuation. Exploratory analyses included time to 3- and 6-month EDSS confirmed disability progression (CDP). Non-pairwise censoring was the primary analysis method; pairwise censoring was the main sensitivity analysis method.FindingsPost-matched cohorts were well-balanced. By non-pairwise censoring, TTFR and ARR were significantly lower in DMF populations versus matched IFN, GA, and TERI, but there was no evidence of difference between DMF and FTY. TTD was similar between DMF and IFN, GA, and TERI, but significantly shorter versus FTY. Time to CDP generally showed no evidence of difference between DMF and comparator populations. Pairwise censored analysis results confirmed the non-pairwise censoring results.InterpretationThese results support previous CE studies in demonstrating relative improvement in real-world effectiveness with DMF versus first-line agents IFN, GA, and TERI, and similar effectiveness versus FTY.Electronic supplementary materialThe online version of this article (10.1007/s00415-018-9083-5) contains supplementary material, which is available to authorized users.