M Guger1, C Enzinger2, F Leutmezer3, J Kraus4,5, S Kalcher6, E Kvas7, T Berger8. 1. Clinic for Neurology 2, Med Campus III., Kepler University Clinic, Linz, Austria. 2. Department of Neurology, Medical University of Graz, Graz, Austria. 3. Department of Neurology, Medical University of Vienna, Vienna, Austria. 4. Department of Laboratory Medicine, Paracelsus Medical University and Salzburger Landeskliniken, Salzburg, Austria. 5. Department of Neurology, Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany. 6. Hermesoft, Data management, Graz, Austria. 7. Hermesoft, Statistics, Graz, Austria. 8. Clinical Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria.
Abstract
OBJECTIVES: To compare the efficacy of natalizumab or fingolimod in a nationwide observational cohort using prospectively collected data. MATERIALS AND METHODS: We included all patients starting treatment with natalizumab or fingolimod documented in the Austrian MS Treatment Registry (AMSTR) from 2011 and staying on therapy for at least 24 months. We used propensity scores for several matching methods and as a covariate in multivariate models to correct for the bias of this non-randomized registry study. RESULTS: The study cohort includes 588 patients with RRMS. Ten patients did not produce a propensity score in the common support region, thus leaving 578 cases for final analyses, 332 in the fingolimod and 246 in the natalizumab group. Mean annualized relapse rates (ARR) during the 24 months observation period were 0.19 under fingolimod and 0.12 under natalizumab treatment (P = .005). No statistical significant differences were found analysing the log-transformed ARR, probability for experiencing a relapse, EDSS progression and EDSS regression. The hazard ratio for switching treatment from fingolimod comparing with natalizumab was 0.36 (95% CI: 0.247-0.523), P < .001. CONCLUSIONS: The generalized linear model (GLM) for relapse count as Poisson distributed dependent variable and propensity score as covariate showed a statistically significant reduction for the mean relapse count in the natalizumab group compared with fingolimod. This effect was smaller in the analyses of log-transformed ARR with propensity score matching, loosing statistical significance although showing the same direction for the effect. We assume that the GLM was the more sensitive model analysing this question.
OBJECTIVES: To compare the efficacy of natalizumab or fingolimod in a nationwide observational cohort using prospectively collected data. MATERIALS AND METHODS: We included all patients starting treatment with natalizumab or fingolimod documented in the Austrian MS Treatment Registry (AMSTR) from 2011 and staying on therapy for at least 24 months. We used propensity scores for several matching methods and as a covariate in multivariate models to correct for the bias of this non-randomized registry study. RESULTS: The study cohort includes 588 patients with RRMS. Ten patients did not produce a propensity score in the common support region, thus leaving 578 cases for final analyses, 332 in the fingolimod and 246 in the natalizumab group. Mean annualized relapse rates (ARR) during the 24 months observation period were 0.19 under fingolimod and 0.12 under natalizumab treatment (P = .005). No statistical significant differences were found analysing the log-transformed ARR, probability for experiencing a relapse, EDSS progression and EDSS regression. The hazard ratio for switching treatment from fingolimod comparing with natalizumab was 0.36 (95% CI: 0.247-0.523), P < .001. CONCLUSIONS: The generalized linear model (GLM) for relapse count as Poisson distributed dependent variable and propensity score as covariate showed a statistically significant reduction for the mean relapse count in the natalizumab group compared with fingolimod. This effect was smaller in the analyses of log-transformed ARR with propensity score matching, loosing statistical significance although showing the same direction for the effect. We assume that the GLM was the more sensitive model analysing this question.
Authors: Michael Guger; Christian Enzinger; Fritz Leutmezer; Jörg Kraus; Stefan Kalcher; Erich Kvas; Thomas Berger Journal: J Neurol Date: 2019-07-16 Impact factor: 4.849
Authors: Michael Guger; Christian Enzinger; Fritz Leutmezer; Jörg Kraus; Stefan Kalcher; Erich Kvas; Thomas Berger Journal: Acta Neurol Scand Date: 2019-04-22 Impact factor: 3.209
Authors: Michael Guger; Christian Enzinger; Fritz Leutmezer; Jörg Kraus; Stefan Kalcher; Erich Kvas; Thomas Berger Journal: J Neurol Date: 2020-04-03 Impact factor: 4.849
Authors: Michael Guger; Christian Enzinger; Fritz Leutmezer; Franziska Di Pauli; Jörg Kraus; Stefan Kalcher; Erich Kvas; Thomas Berger Journal: J Neurol Date: 2021-04-22 Impact factor: 4.849