A method to compare prospective and historical cohorts to evaluate drug effects. Application to the analysis of early treatment effectiveness of intramuscular interferon-β1a in multiple sclerosis patients

Massimiliano Mirabella, Giulia Mallucci, Francesco Patti, Vincenzo Brescia Morra, Maria Buccafusca, Lucia Moiola, Maria Pia Amato, Elisabetta Ferraro, Maria Trojano, Mauro Zaffaroni, Gianluca Moscato, Katrin Plewnia, Valentina Zipoli, Elisa Puma, Roberto Bergamaschi

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

Abstract

Background: Disease modifying therapy have changed the natural evolution of multiple sclerosis (MS), with efficacy demonstrated in randomized clinical trials. Standard-of-care effectiveness is needed to complement clinical trial data and highlight outcomes in real-world practice, but comparing prospective patients with historical cohorts likely introduces biases. To address these potential biases, assigning a patient with a score that expresses his/her disease prognosis before starting a therapy may make it possible to evaluate the unbiased ability of the therapy to modify disease natural history. Thus, we aimed at analyzing the effectiveness of intramuscular interferon-β1a (im IFN-β1a) matching by BREMSO score (Bayesian Risk Estimate for Multiple Sclerosis at Onset) a prospective real-world cohort of treated patients with a historical cohort of untreated patients. Material and methods: We observed 108 newly diagnosed, treatment naïve MS patients over 12 months of treatment with im IFN-β1a. BREMSO score was used to assign a value to each patient, giving the real-world treated patients comparable with the Historical untreated patients, on the basis of the same risk to have unfavorable evolution. Results: A significantly higher percentage of relapse-free patients is observed in IFN-β1a treated cohort vs. Historical untreated cohort (79.6% vs. 59.3%, p < 0.01). Clinical relapses risk is reduced by 2.2 times in treated patients (p = 0.01). Conclusions: We propose a promising method to manage observational data in a relatively unbiased way, in order to analyze real-world treatment effectiveness.
Lingua originaleEnglish
pagine (da-a)101952-N/A
RivistaMultiple Sclerosis and Related Disorders
Volume40
DOI
Stato di pubblicazionePubblicato - 2020

Keywords

  • BREMSO
  • Bayesian analysis
  • Beta-interferon
  • Multiple sclerosis
  • Prognosis natural history

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