Evidence map›Paper›PMID 41206399›Full record

ReviewJournal of neurology2025

Big multiple sclerosis data network: novel modelling approaches for real-world data analysis.

M Trojano, P Iaffaldano, M Copetti, J Drahota, L Forsberg, E F Mouresan, L Pontieri, T Spelman, N Toschi, H Butzkueven and 7 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Journal of neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Big data in multiple sclerosis.Current opinion in neurology · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

M Trojano *Department of Translational Biomedicine and Neurosciences -DiBrain, University of Bari "Aldo Moro", Piazza Umberto I, 70121, Bari, Italy. maria.trojano@uniba.it.ORCID http://orcid.org/0000-0002-6329-8946
P Iaffaldano *Department of Translational Biomedicine and Neurosciences - DiBraiN, University of Bari Aldo Moro, Bari, Italy.ORCID http://orcid.org/0000-0003-2308-1731
M CopettiUnit of Biostatistics, Fondazione IRCCS-"Casa Sollievo Della Sofferenza", San Giovanni Rotondo, Italy.
J DrahotaDepartment of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University in Prague and General University Hospital, Prague, Czech Republic.
L ForsbergDepartment of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden.ORCID http://orcid.org/0000-0002-3536-1813
E F MouresanDepartment of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden.
L PontieriThe Danish Multiple Sclerosis Registry, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.ORCID http://orcid.org/0000-0002-1506-3827
T SpelmanDepartment of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia.
N ToschiMedical Physics Section - Department of Biomedicine and Prevention, University of Rome, Tor Vergata, Italy.
H ButzkuevenDepartment of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia.
A GlaserDepartment of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden.
J HillertDepartment of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden.
D HorakovaDepartment of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University in Prague and General University Hospital, Prague, Czech Republic.ORCID http://orcid.org/0000-0003-1915-0036
M MagyariThe Danish Multiple Sclerosis Registry, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.ORCID http://orcid.org/0000-0002-0972-5222
S VukusicService de Neurologie, sclérose en plaques, pathologies de la myéline et neuro-inflammation, Hospices Civils de Lyon, Hôpital Neurologique Pierre Wertheimer, 69677, Bron, France.
G Lucisano *CORESEARCH-Center for Outcomes Research and Clinical Epidemiology, Pescara, Italy.
T Kalincik *Neuroimmunology Centre, Department of Neurology, Royal Melbourne Hospital, Melbourne, 3000, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe 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.

methodsThe 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.

resultsBayesian 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.

conclusionsThis 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.

Indexed as

Big DataData AnalysisDatabases, FactualMultiple SclerosisRegistriesBayes TheoremHumansMachine LearningBig DataComputational methodologiesMultiple SclerosisReal-world dataStatistical methodologies

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.