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  • Review Article
  • Published:

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

Nature Reviews Neurology volume 13, pages 105–118 (2017)Cite this article

Subjects

Key Points

  • The repertoire of disease-modifying therapies for relapsing–remitting multiple sclerosis (MS) has broadened greatly in the past decade

  • Evidence-based recommendations from randomized clinical trials are insufficient to guide choices between most available MS drugs

  • The combination of increasing worldwide availability of and access to large MS registries and databases and the growing ability to share and analyse large datasets is enabling real-world observational studies to be conducted

  • Observational real-world studies are providing insights into predictors of MS treatment response, comparative effectiveness of disease-modifying therapies, and long-term treatment effectiveness that is useful for directing daily clinical practice

  • Several new statistical methods are available, and continue to evolve, to minimize biases and limitations of real-world observational studies, thereby optimizing their validity and reliability

  • In future, datasets from individual MS databases and registries should be aggregated into big data algorithms to develop new tools that will enable the implementation of personalized medicine

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.

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Authors and Affiliations

  1. Department of Basic Medical Sciences, Neurosciences and Sense Organs, University of Bari “Aldo Moro”, Piazza G. Cesare 11, Bari, 70124, Italy

    Maria Trojano & Pietro Iaffaldano

  2. 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, Barcelona, 08035, Spain

    Mar Tintore & Xavier Montalban

  3. Department of Clinical Neuroscience, Karolinska Institutet, Tomtebodavägen 18A, Stockholm, S-17177, Solna, Sweden

    Jan Hillert

  4. Department of Medicine, and Department of Neurology, University of Melbourne, Royal Melbourne Hospital, Grattan Street, Parkville, 3050, VIC, Australia

    Tomas Kalincik, Tim Spelman & Helmut Butzkueven

  5. Department of Health Sciences (DISSAL), University of Genoa, Via Pastore 1, Genoa, 16132, Italy

    Maria Pia Sormani

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  1. Maria Trojano
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  9. Helmut Butzkueven
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Contributions

Maria Trojano coordinated the article and edited the manuscript before submission. All authors made substantial contributions to writing the article and discussion of the content, and reviewed the manuscript before submission.

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Correspondence to Maria Trojano.

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Competing interests

Maria Trojano has served on scientific Advisory Boards for Almirall, Biogen, Genzyme, Novartis and Roche; has received speaker honoraria from Almirall, Bayer, Biogen, Genzyme, Merck Serono, Novartis, Sanofi and Teva Pharmaceuticals; and has received research grants for her Institution from Biogen, Merck Serono and Novartis. Mar Tintore has received compensation for consulting services and speaking from Bayer, Biogen, Merck Serono, Novartis, Sanofi and Teva Pharmaceuticals. Xavier Montalban has received speaking honoraria and travel expenses for scientific meetings, has been a steering committee member of clinical trials or participated in advisory boards of clinical trials with Almirall, Bayer, Biogen, Genentech, Genzyme, Merck Serono, Novartis, Sanofi and Teva Pharmaceuticals. Jan Hillert has received honoraria for serving on advisory boards for Biogen, Genzyme and Novartis, and has received speaker's fees from Bayer, Biogen, Genzyme, Merck Serono, Novartis, and Teva Pharmaceuticals. He has served as principal investigator for projects sponsored by, or received unrestricted research support from Bayer, Biogen, Merck Serono, Novartis and Teva Pharmaceuticals. Tomas Kalincik has served on scientific advisory boards for Biogen, Genzyme, Merck, Novartis and Roche; has received conference travel support and/or speaker honoraria from BioCSL, Biogen, Genzyme, Merck, Novartis, Sanofi, Teva Pharmaceuticals and WebMD Global; and has received research support from Biogen. Pietro Iaffaldano has served on scientific advisory boards for Bayer and Biogen, and has received funding for travel and/or speaker honoraria from Biogen, Novartis, Sanofi and Teva Pharmaceuticals. Tim Spelman has received travel support, speaker honoraria and compensation for serving on advisory boards from Biogen and Novartis. Maria Pia Sormani received consulting fees from Biogen, GeNeuro, Genzyme, Merck Serono, Novartis, Roche, Teva Pharmaceuticals and Vertex. Helmut Butzkueven has served on scientific advisory boards for Biogen, Novartis and Sanofi and has received conference travel support from Biogen, Novartis and Sanofi. He serves on steering committees for trials conducted by Biogen and Novartis and has received research support from Biogen, Merck Serono and Novartis.

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Glossary

Prognostic nomograms

Graphical prediction tools designed to assess the risk of future event based on specific patient and disease characteristics.

Least absolute shrinkage and selection operator (LASSO) procedure

A regression analysis method that enhances the prediction accuracy and interpretability of the statistical model.

Bayesian hierarchical metaregression model

A metaregression is a meta-analysis designed to assess factors associated with the size of the treatment effect; Bayesian hierarchical modelling allows estimation of the parameters of the metaregression.

Inverse probability of treatment weighting

A weighting method that uses propensity scores to derive a synthetic sample within which the distribution of baseline prognostic confounding variables is independent of the treatment assignment; the weight given to a patient is the inverse of the probability that he or she would receive the treatment that he or she actually did receive.

Progressive multifocal leukoencephalopathy

A viral encephalitis caused by JC virus, predominantly involving white matter and reported in patients being treated with certain immunosuppressive and immunomodulatory therapies.

Bayesian approach

A method of statistical inference that allows prior information about a population parameter to be combined with evidence from a sample to guide the statistical inference process.

Continuous Markov model

A model used in economics that is based on a stochastic process with the Markov property, which defines serial dependence between adjacent periods only; the model can be used to describe systems in which the next event depends only on the current state of the system.

Multilevel model

A statistical model in which parameters vary at more than one level. Observational studies in which many observations are made per subject include two levels of variability: the variability between subjects and the variability within each subject over time.

Cost–utility ratios

The outcomes of cost–utility analysis, a form of financial analysis used to guide decisions. The cost–utility ratio estimates the ratio between the cost of a health-related intervention and the benefit it produces in terms of the number of years lived in full health by the beneficiaries.

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Trojano, M., Tintore, M., Montalban, X. et al. Treatment decisions in multiple sclerosis — insights from real-world observational studies. Nat Rev Neurol 13, 105–118 (2017). https://doi.org/10.1038/nrneurol.2016.188

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