J Neurol. 2025 Nov 8;272(12):754. doi: 10.1007/s00415-025-13439-9.
ABSTRACT
OBJECTIVE: The objective of this study is to present a report from the Big Multiple Sclerosis Data (BMSD) statistics workshop (Bari – Italy, June 2023) which focused on advanced statistical approaches for real-world data (RWD) analyses in multiple sclerosis (MS). The report emphasises the application of these approaches in predicting individual treatment response, assessing comparative effectiveness and safety of therapies and their sequences, and harmonizing data for large-scale federated analyses.
METHODS: The BMSD network, comprising five national registries and the international MSBase database (> 350,000 total patients), convened in June 2023 in Bari (Italy) to review methodological advances in RWD analysis. Experts discussed strengths, limitations, and regulatory implications of frequentist, Bayesian, and machine learning (ML) approaches, with case studies on treatment response modelling, comparative effectiveness, safety surveillance, and Common Data Model (CDM)-based federated learning.
RESULTS: Bayesian and ML techniques, integrated with causal inference frameworks, can improve personalized predictions of treatment benefit and risk by using high-dimensional longitudinal data. Propensity score-based methods and marginal structural models remain essential for minimizing confounding in comparative analyses, but require rigorous diagnostics and sensitivity analyses. Adoption of a CDM facilitates harmonization of heterogeneous datasets, while federated learning enables privacy-preserving, multi-jurisdictional collaboration. Together, these innovations address key challenges in studying treatment sequences, rare adverse events, and underrepresented patient groups.
CONCLUSIONS: This workshop report highlights how advanced statistical and computational methodologies enhance the robustness, interpretability, and regulatory relevance of MS RWD studies. By promoting the integration of complementary statistical and computational approaches within harmonized data infrastructures, the BMSD network is positioned to accelerate the translation of real-world evidence into precision medicine for MS.
PMID:41206399 | DOI:10.1007/s00415-025-13439-9
