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Nat Rev Neurol. 2017 Feb;13(2):105-118. doi: 10.1038/nrneurol.2016.188. Epub 2017 Jan 13.

Treatment decisions in multiple sclerosis - insights from real-world observational studies.

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Department of Basic Medical Sciences, Neurosciences and Sense Organs, University of Bari "Aldo Moro", Piazza G. Cesare 11, 70124, Bari, Italy.
Department of Neurology-Neuroimmunology and Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Passeig Vall d'Hebron 119-129, 08035, Barcelona, Spain.
Department of Clinical Neuroscience, Karolinska Institutet, Tomtebodavägen 18A, S-17177, Solna, Stockholm, Sweden.
Department of Medicine, University of Melbourne, and Department of Neurology, Royal Melbourne Hospital, Grattan Street, Parkville, VIC 3050, Australia.
Department of Health Sciences (DISSAL), University of Genoa, Via Pastore 1, 16132, Genoa, Italy.


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 for MEDLINE]

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