ReviewNature reviews. Neurology2017
Treatment decisions in multiple sclerosis - insights from real-world observational studies.
Review in Nature reviews. Neurology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 3 of them syntheses that pooled 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.
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.
Who cites it
92 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Rituximab for people with multiple sclerosis.The Cochrane database of systematic reviews · 2025Pooled it
- Azathioprine for people with multiple sclerosis.The Cochrane database of systematic reviews · 2024Pooled it
- Rituximab for people with multiple sclerosis.The Cochrane database of systematic reviews · 2021Pooled it
- Cholecalciferol in relapsing-remitting MS: A randomized clinical trial (CHOLINE).Neurology(R) neuroimmunology & neuroinflammation · 2019Trial
- Treatment persistence in paediatric-onset multiple sclerosis: A Swedish nationwide registry study.Multiple sclerosis (Houndmills, Basingstoke, England) · 2026Article
- Big data in multiple sclerosis.Current opinion in neurology · 2026Review
- Development of a New Patient-Reported Outcome to Measure Fatigue in Patients with Multiple Sclerosis.Nursing reports (Pavia, Italy) · 2026Article
- Big multiple sclerosis data network: novel modelling approaches for real-world data analysis.Journal of neurology · 2025Review
- Integrating big data and artificial intelligence to predict progression in multiple sclerosis: challenges and the path forward.Journal of neuroengineering and rehabilitation · 2025Review
- Disease-modifying treatment and disability progression in subclasses of patients with primary progressive MS: results from the Big MS Data Network.Journal of neurology, neurosurgery, and psychiatry · 2025Article
- Laser treatment for urinary incontinence in elite female athletes analyzed using a discrete mathematics approach.Scientific reports · 2025Article
- CONFIDENCE treatment success: long-term real-world effectiveness and safety of ocrelizumab in Germany.Frontiers in neurology · 2025Article
- Real-world persistence of multiple sclerosis disease-modifying therapies.European journal of neurology · 2024Article
- Big Multiple Sclerosis Data network: an international registry research network.Journal of neurology · 2024Article
- Challenges in Diagnosis and Therapeutic Strategies in Late-Onset Multiple Sclerosis.Journal of personalized medicine · 2024Review
- Utilization of Ocrelizumab within Different Treatment Strategies for Multiple Sclerosis: A 5-Year Population-Based Study.Neurology international · 2024Article
- Recent Advances in Biosensors for Diagnosis of Autoimmune Diseases.Sensors (Basel, Switzerland) · 2024Review
- Five-year efficacy outcomes of ocrelizumab in relapsing multiple sclerosis: A propensity-matched comparison of the OPERA studies with other disease-modifying therapies in real-world lines of treatments.Journal of central nervous system disease · 2024Article
- The arisal of data spaces: why I am excited and worried.Frontiers in immunology · 2024Article
- Observational studies of treatment effectiveness in neurology.Brain : a journal of neurology · 2023Review
32 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The complexity of multiple sclerosis (MS) treatment means that doctors and decision-makers need the best available evidence to make the best decisions for patient care. Randomized controlled trials (RCTs) are accepted as the gold standard for assessing the efficacy and safety of any new drug, but conclusions of these trials do not always aid in daily decision-making processes. Indeed, RCTs are usually conducted in ideal conditions, so can measure efficacy only in restricted and unrepresentative populations. In the past decade, a growing number of MS databases and registries have started to produce long-term outcome data from large cohorts of patients with MS treated with disease-modifying therapies in real-world settings. Such observational studies are addressing issues that are otherwise difficult or impossible to study. In this Review, we focus on the most recently published observational studies designed to identify predictors of poor outcome and treatment response or failure, and to evaluate the relative and long-term effectiveness of currently used MS treatments. We also outline the statistical approaches that are most commonly used to reduce bias and limitations in these studies, and the challenges associated with the use of 'big MS data' to facilitate the implementation of personalized medicine in MS.
Indexed as
Identifiers
28084327What OpenQuestion holds
Registered trials
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.