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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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References

  1. Rothwell, P. M. External validity of randomised controlled trials: “to whom do the results of this trial apply?” Lancet 365, 82–93 (2005).

    Article  PubMed  Google Scholar 

  2. Dekkers, O. M., von Elm, E., Algra, A., Romijn, J. A. & Vandenbroucke, J. P. How to assess the external validity of therapeutic trials: a conceptual approach. Int. J. Epidemiol. 39, 89–94 (2010).

    Article  CAS  PubMed  Google Scholar 

  3. Sormani, M. P. & Bruzzi, P. Can we measure long-term treatment effects in multiple sclerosis? Nat. Rev. Neurol. 11, 176–182 (2015).

    Article  PubMed  Google Scholar 

  4. Kalincik, T. & Butzkueven, H. Observational data: understanding the real MS world. Mult. Scler. 22, 1642–1648 (2016).

    Article  PubMed  Google Scholar 

  5. Ioannidis, J. P. Indirect comparisons: the mesh and mess of clinical trials. Lancet 368, 1470–1472 (2006).

    Article  PubMed  Google Scholar 

  6. Van Luijn, J. C., Gribnau, F. W. & Leufkens, H. G. Availability of comparative trials for the assessment of new medicines in the European Union at the moment of market authorization. Br. J. Clin. Pharmacol. 63, 159–162 (2007).

    Article  PubMed  Google Scholar 

  7. Confavreux, C., Compston, D. A., Hommes, O. R., McDonald, W. I. & Thompson, A. J. EDMUS, a European database for multiple sclerosis. J. Neurol. Neurosurg. Psychiatry 55, 671–676 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Butzkueven, H. et al. MSBase: an international, online registry and platform for collaborative outcomes research in multiple sclerosis. Mult. Scler. 12, 769–774 (2006).

    Article  CAS  PubMed  Google Scholar 

  9. Trojano, M. et al. Italian Multiple Sclerosis Database Network. Neurol. Sci. 27 (Suppl. 5), S358–S361 (2006).

    Article  PubMed  Google Scholar 

  10. Myhr, K. M., Grytten, N. & Aarseth, J. H. The Norwegian Multiple Sclerosis Registry and Biobank. Acta Neurol. Scand. Suppl. 195, 20–23 (2012).

    Article  Google Scholar 

  11. Flachenecker, P. et al. EUReMS Consortium. Multiple sclerosis registries in Europe — results of a systematic survey. Mult. Scler. 20, 1523–1532 (2014).

    Article  PubMed  Google Scholar 

  12. Hillert, J. & Stawiarz, L. The Swedish MS registry — clinical support tool and scientific resource. Acta Neurol. Scand. 132, 11–19 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Koch-Henriksen, N., Magyari, M. & Laursen, B. Registers of multiple sclerosis in Denmark. Acta Neurol. Scand. 132, 4–10 (2015).

    Article  CAS  PubMed  Google Scholar 

  14. Cook, J. A. & Collins, G. S. The rise of big clinical databases. Br. J. Surg. 102, e93–e101 (2015).

    Article  CAS  PubMed  Google Scholar 

  15. Benson, K. & Hartz, A. J. A comparison of observational studies and randomized, controlled trials. N. Engl. J. Med. 342, 1878–1886 (2000).

    Article  CAS  PubMed  Google Scholar 

  16. Concato, J., Shah, N. & Horwitz, R. I. Randomized, controlled trials, observational studies, and the hierarchy of research designs. N. Engl. J. Med. 342, 1887–1892 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Porter, M. E., Larsson, S. & Lee, T. H. Standardizing patient outcomes measurement. N. Engl. J. Med. 374, 504–506 (2016).

    Article  CAS  PubMed  Google Scholar 

  18. Kitsios, G. D. Propensity score studies are unlikely to underestimate treatment effects in critical care medicine: a critical reanalysis. J. Clin. Epidemiol. 68, 467–469 (2015).

    Article  PubMed  Google Scholar 

  19. Rosenbaum, P. R. & Rubin, D. B. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55 (1983).

    Article  Google Scholar 

  20. Austin, P. C. The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies. Med. Decis. Making 29, 661–677 (2009).

    Article  PubMed  Google Scholar 

  21. Trojano, M. et al. Observational studies: propensity score analysis of non-randomized data. Int. MS J. 16, 90–97 (2009).

    CAS  PubMed  Google Scholar 

  22. Trojano, M. in Multiple Sclerosis Therapeutics 4th edn Ch. 21 (eds Cohen, J. A. & Rudick, R.) 244–252 (Cambridge Univ. Press, 2011).

    Book  Google Scholar 

  23. Glynn, R. J., Schneeweiss, S. & Stürmer, T. Indications for propensity scores and review of their use in pharmacoepidemiology. Basic Clin. Pharmacol. Toxicol. 98, 253–259 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Austin, P. C. Propensity-score matching in the cardiovascular surgery literature from 2004 to 2006: a systematic review and suggestions for improvement. J. Thorac. Cardiovasc. Surg. 134, 1128–1135 (2007).

    Article  PubMed  Google Scholar 

  25. Stenestrand, U., Wallentin, L. & Swedish Register of Cardiac Intensive Care (RIKS-HIA). Early statin treatment following acute myocardial infarction and 1-year survival. JAMA 285, 430–436 (2001).

    Article  CAS  PubMed  Google Scholar 

  26. Gum, P. A., Thamilarasan, M., Watanabe, J., Blackstone, E. H. & Lauer, M. S. Aspirin use and all-cause mortality among patients being evaluated for known or suspected coronary artery disease: a propensity analysis. JAMA 286, 1187–1194 (2001).

    Article  CAS  PubMed  Google Scholar 

  27. Kern, L. M. et al. Association between screening for osteoporosis and the incidence of hip fracture. Ann. Intern. Med. 142, 173–181 (2005).

    Article  PubMed  Google Scholar 

  28. Schneeweiss, S. et al. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology. 20, 512–522 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Rosenbaum, P. R. Discussing hidden bias in observational studies. Ann. Intern. Med. 115, 901–905 (1991).

    Article  CAS  PubMed  Google Scholar 

  30. Lin, D. Y., Psaty, B. M. & Krommal, R. A. Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics 54, 948–963 (1998).

    Article  CAS  PubMed  Google Scholar 

  31. Karim, M. E. et al. Marginal structural Cox models for estimating the association between β-interferon exposure and disease progression in a multiple sclerosis cohort. Am. J. Epidemiol. 180, 160–171 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Westreich, D., Cole, S. R., Schisterman, E. F. & Platt, R. W. A simulation study of finite-sample properties of marginal structural Cox proportional hazards models. Stat. Med. 31, 2098–2109 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Havercroft, W. G. & Didelez, V. Simulating from marginal structural models with time-dependent confounding. Stat. Med. 31, 4190–4206 (2012).

    Article  CAS  PubMed  Google Scholar 

  34. Debray, T., Moons, K. G., Ahmed, I., Koffijberg, H. & Riley, R. D. A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis. Stat. Med. 32, 3158–3180 (2013).

    Article  PubMed  Google Scholar 

  35. Ahmed, I., Debray, T. P., Moons, K. G. & Riley, R. D. Developing and validating risk prediction models in an individual participant data meta-analysis. BMC Med. Res. Methodol. 14, 3 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Zhao, L., Tian, L., Cai, T., Claggett, B. & Wei, L. J. Effectively selecting a target population for a future comparative study. J. Am. Stat. Assoc. 108, 527–539 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Verde, P. E., Ohmann, C., Morbach, S. & Icks, A. Bayesian evidence synthesis for exploring generalizability of treatment effects: a case study of combining randomized and non-randomized results in diabetes. Stat. Med. 35, 1654–1675 (2016).

    Article  PubMed  Google Scholar 

  38. Scott, I. A. & Attia, J. Cautionary tales in the interpretation of observational studies of effects of clinical interventions. Intern. Med. J. http://dx.doi.org/10.1111/imj.13167 (2016). This article proposes criteria for identifying high quality observational studies.

  39. Arts, D. G., De Keizer, N. F. & Scheffer, G. J. Defining and improving data quality in medical registries: a literature review, case study, and generic framework. J. Am. Med. Inform. Assoc. 9, 600–611 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Christiansen, D. H., Hosking, J. D., Dannenberg, A. L. & Williams, O. D. Computer-assisted data collection in multicenter epidemiologic research. The Atherosclerosis Risk Communities Study. Control. Clin. Trials 11, 101–115 (1990).

    Article  CAS  PubMed  Google Scholar 

  41. Weiskopf, N. G. & Weng, C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J. Am. Med. Inform. Assoc. 20, 144–151 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Goudar, S. et al. Data quality monitoring and performance metrics of a prospective, population-based observational study of maternal and newborn health in low resource settings. Reprod. Health 12 (Suppl. 2), S2 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Kalincik, T. et al. Data quality evaluation for observational multiple sclerosis registries. Mult. Scler. http://dx.doi.org/10.1177/1352458516662728 (2016).

  44. Tintore, M. et al. Defining high, medium and low impact prognostic factors for developing multiple sclerosis. Brain 138, 1863–1874 (2015). The largest single-centre prospective study evaluating prognostic factors in patients with clinically isolated syndrome suggestive of MS.

    Article  PubMed  Google Scholar 

  45. Jokubaitis, V. G. et al. Predictors of disability worsening in clinically isolated syndrome. Ann. Clin. Transl Neurol. 2, 479–491 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Marrie, R. A. et al. Recommendations for observational studies of comorbidity in multiple sclerosis. Neurology 86, 1446–1453 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Trojano, M. et al. Real-life impact of early interferonβ therapy in relapsing multiple sclerosis. Ann. Neurol. 66, 513–520 (2009).

    Article  CAS  PubMed  Google Scholar 

  48. Río, J. et al. Defining the response to interferon-beta in relapsing-remitting multiple sclerosis patients. Ann. Neurol. 59, 344–352 (2006).

    Article  CAS  PubMed  Google Scholar 

  49. Río, J. et al. Measures in the first year of therapy predict the response to interferon beta in MS. Mult. Scler. 15, 848–853 (2009).

    Article  PubMed  Google Scholar 

  50. Bermel, R. A. et al. Predictors of long-term outcome in multiple sclerosis patients treated with interferon β. Ann. Neurol. 73, 95–103 (2013).

    Article  CAS  PubMed  Google Scholar 

  51. Jokubaitis, V. G. et al. Predictors of long-term disability accrual in relapse-onset multiple sclerosis. Ann. Neurol. 80, 89–100 (2016).

    Article  PubMed  Google Scholar 

  52. Horakova, D. et al. Early predictors of non-response to interferon in multiple sclerosis. Acta Neurol. Scand. 126, 390–397 (2012).

    Article  CAS  PubMed  Google Scholar 

  53. Uher, T. et al. Combining clinical and MRI predictors enhances prediction of 12-year disability in multiple sclerosis. Mult. Scler. http://dx.doi.org/10.1177/1352458516642314 (2016).

  54. Uher, T. et al. Early magnetic resonance imaging predictors of clinical progression after 48 months in clinically isolated syndrome patients treated with intramuscular interferon β-1a. Eur. J. Neurol. 22, 1113–1123 (2015).

    Article  CAS  PubMed  Google Scholar 

  55. Río, J. et al. Evaluating the response to glatiramer acetate in relapsing-remitting multiple sclerosis (RRMS) patients. Mult. Scler. 20, 1602–1608 (2014).

    Article  CAS  PubMed  Google Scholar 

  56. Sormani, M. P. et al. Scoring treatment response in patients with relapsing multiple sclerosis. Mult. Scler. 19, 605–612 (2013).

    Article  CAS  PubMed  Google Scholar 

  57. Prosperini, L. et al. Interferon beta failure predicted by EMA criteria or isolated MRI activity in multiple sclerosis. Mult. Scler. 20, 566–576 (2014).

    Article  CAS  PubMed  Google Scholar 

  58. Dobson, R., Rudick, R. A., Turner, B., Schmierer, K. & Giovannoni, G. Assessing treatment response to interferon-β: is there a role for MRI? Neurology 82, 248–254 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Sormani, M. P. et al. Assessing response to interferon-β in a multicenter dataset of patients with MS. Neurology 87, 134–140 (2016). The largest study assessing MRI criteria for predicting IFN-β treatment non-response in real-world studies.

    Article  CAS  PubMed  Google Scholar 

  60. Rio, J. et al. Clinical markers of long-term disability in RRMS patients treated with interferon beta [poster]. Mult. Scler. 20 (Suppl. 1), P285 (2014).

    Google Scholar 

  61. Río, J. et al. Relationship between MRI lesion activity and response to IFN-beta in relapsing-remitting multiple sclerosis patients. Mult. Scler. 14, 479–484 (2008).

    Article  PubMed  Google Scholar 

  62. Altay, E. E. et al. Reliability of classifying multiple sclerosis disease activity using magnetic resonance imaging in a multiple sclerosis clinic. JAMA Neurol. 70, 338–344 (2013).

    Article  PubMed Central  Google Scholar 

  63. Wattjes, M. P. et al. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis — establishing disease prognosis and monitoring patients. Nat. Rev. Neurol. 11, 597–606 (2015).

    CAS  PubMed  Google Scholar 

  64. Rudick, R. A., Lee, J. C., Simon, J., Ransohoff, R. M. & Fisher, E. Defining interferon beta response status in multiple sclerosis patients. Ann. Neurol. 56, 548–555 (2004).

    Article  CAS  PubMed  Google Scholar 

  65. Bevan, C. J. & Cree, B. A. Disease activity free status: a new end point for a new era in multiple sclerosis clinical research? JAMA Neurol. 71, 269–270 (2014).

    Article  PubMed  Google Scholar 

  66. Kappos, L. et al. Inclusion of brain volume loss in a revised measure of 'no evidence of disease activity' (NEDA-4) in relapsing–remitting multiple sclerosis. Mult. Scler. 22, 1297–1305 (2016).

    Article  PubMed  Google Scholar 

  67. Sormani, M. P., Arnold, D. L. & De Stefano, N. Treatment effect on brain atrophy correlates with treatment effect on disability in multiple sclerosis. Ann. Neurol. 75, 43–49 (2014).

    Article  PubMed  Google Scholar 

  68. Stangel, M., Penner, I., Kallmann, B. A., Lukas, C. & Kieseier, B. C. Towards the implementation of 'no evidence of disease activity' in multiple sclerosis treatment: the multiple sclerosis decision model. Ther. Adv. Neurol. Disord. 8, 3–13 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Kuhle, J. et al. Fingolimod and CSF neurofilament light chain levels in relapsing–remitting multiple sclerosis. Neurology 84, 1639–1643 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Rotstein, D. L., Healy, B. C., Malik, M. T., Chitnis, T. & Weiner, H. L. Evaluation of no evidence of disease activity in a 7-year longitudinal multiple sclerosis cohort. JAMA Neurol. 72, 152–158 (2015).

    Article  PubMed  Google Scholar 

  71. Jacobs, L. D. et al. Intramuscular interferon beta-1a therapy initiated during a first demyelinating event in multiple sclerosis. CHAMPS Study Group. N. Engl. J. Med. 343, 898–904 (2000).

    Article  CAS  PubMed  Google Scholar 

  72. Comi, G. et al. Effect of early interferon treatment on conversion to definite multiple sclerosis: a randomised study. Lancet 357, 1576–1582 (2001).

    Article  CAS  PubMed  Google Scholar 

  73. Kappos, L. et al. Long-term subcutaneous interferon beta-1a therapy in patients with relapsing-remitting MS. Neurology 67, 944–953 (2006).

    Article  CAS  PubMed  Google Scholar 

  74. Polman, C. et al. Subgroups of the BENEFIT study: risk of developing MS and treatment effect of interferon beta-1b. J. Neurol. 255, 480–487 (2008).

    Article  CAS  PubMed  Google Scholar 

  75. Comi, G. et al. Effect of glatiramer acetate on conversion to clinically definite multiple sclerosis in patients with clinically isolated syndrome (PreCISe study): a randomised, double-blind, placebo-controlled trial. Lancet 374, 1503–1511 (2009).

    Article  CAS  PubMed  Google Scholar 

  76. Kappos, L. et al. Long-term effect of early treatment with interferon beta-1b after a first clinical event suggestive of multiple sclerosis: 5-year active treatment extension of the phase 3 BENEFIT trial. Lancet Neurol. 8, 987–997 (2009).

    Article  CAS  PubMed  Google Scholar 

  77. Kinkel, P. R. et al. Association between immediate initiation of intramuscular interferon beta-1a at the time of a clinically isolated syndrome and long-term outcomes: a 10-year follow-up of the Controlled High-Risk Avonex Multiple Sclerosis Prevention Study in Neurological Surveillance. Arch. Neurol. 69, 183–190 (2012).

    Article  PubMed  Google Scholar 

  78. Miller, A. E. et al. Oral teriflunomide for patients with a first clinical episode suggestive of multiple sclerosis (TOPIC): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Neurol. 13, 977–986 (2014).

    Article  CAS  Google Scholar 

  79. Boster, A. et al. Disease activity in the first year predicts longer-term clinical outcomes in the pooled population of the phase III FREEDOMS and FREEDOMS II studies [poster]. Neurology 84 (14 Suppl.), P7.239 (2015).

    Google Scholar 

  80. Giovannoni, G. et al. Is it time to target no evident disease activity (NEDA) in multiple sclerosis? Mult. Scler. Relat. Disord. 4, 329–333 (2015).

    Article  PubMed  Google Scholar 

  81. Filippini, G. et al. Immunomodulators and immunosuppressants for multiple sclerosis: a network meta-analysis. Cochrane Database Syst. Rev. 6, CD008933 (2013).

    Google Scholar 

  82. Kalincik, T. et al. Comparative effectiveness of glatiramer acetate and interferon beta formulations in relapsing-remitting multiple sclerosis. Mult. Scler. 21, 1159–1171 (2015).

    Article  CAS  PubMed  Google Scholar 

  83. Sekhon, J. Multivariate and propensity score matching software with automated balance optimization: the matching package for R. J. Stat. Software 42, 7 (2011).

    Article  Google Scholar 

  84. Spelman, T. et al. Comparative efficacy of first-line natalizumab versus IFNβ or glatiramer acetate in relapsing-remitting MS. Neurol. Clin. Pract. 6, 102–115 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Rassen, J. A. et al. One-to-many propensity score matching in cohort studies. Pharmacoepidemiol. Drug Saf. 21 (Suppl. 2), 69–80 (2012).

    Article  PubMed  Google Scholar 

  86. Winkelmann, A., Loebermann, M., Reisinger, E. C., Hartung, H. P. & Zettl, U. K. Disease-modifying therapies and infectious risks in multiple sclerosis. Nat. Rev. Neurol. 12, 217–233 (2016).

    Article  CAS  PubMed  Google Scholar 

  87. Lublin, F. D., Baier, M. & Cutter, G. Effect of relapses on development of residual deficit in multiple sclerosis. Neurology 61, 1528–1532 (2003).

    Article  PubMed  Google Scholar 

  88. Hirst, C. et al. Contribution of relapses to disability in multiple sclerosis. J. Neurol. 255, 280–287 (2008).

    Article  PubMed  Google Scholar 

  89. Stewart, T. et al. Contribution of different relapse phenotypes to disability in multiple sclerosis. Mult. Scler. http://dx.doi.org/10.1177/1352458516643392 (2016).

  90. Khatri, B. et al. Comparison of fingolimod with interferon beta-1a in relapsing-remitting multiple sclerosis: a randomised extension of the TRANSFORMS study. Lancet Neurol. 10, 520–529 (2011).

    Article  CAS  PubMed  Google Scholar 

  91. Coles, A. J. et al. Alemtuzumab for patients with relapsing multiple sclerosis after disease-modifying therapy: a randomised controlled phase 3 trial. Lancet 380, 1829–1839 (2012).

    Article  CAS  PubMed  Google Scholar 

  92. Spelman, T. et al. Comparative efficacy of switching to natalizumab in active multiple sclerosis. Ann. Clin. Transl Neurol. 2, 373–387 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. He, A. et al. Comparison of switch to fingolimod or interferon beta/glatiramer acetate in active multiple sclerosis. JAMA Neurol. 72, 405–413 (2015).

    Article  PubMed  Google Scholar 

  94. Schoenfeld, D. Chi-squared goodness-of-fit tests for the proportional hazards regression model. Biometrika 67, 145–153 (1980).

    Article  Google Scholar 

  95. Kalincik, T. et al. Switch to natalizumab versus fingolimod in active relapsing-remitting multiple sclerosis. Ann. Neurol. 77, 425–435 (2015). The first comparative study evaluating the effectiveness of natalizumab and fingolimod after first-line treatment failure.

    Article  CAS  PubMed  Google Scholar 

  96. Baroncini, D. et al. Natalizumab versus fingolimod in patients with relapsing-remitting multiple sclerosis non-responding to first-line injectable therapies. Mult. Scler. 22, 1315–1326 (2016).

    Article  CAS  PubMed  Google Scholar 

  97. Barbin, L. et al. Comparative efficacy of fingolimod versus natalizumab: a French multicenter observational study. Neurology 86, 771–778 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Koch-Henriksen, N., Magyari, M., Sellebjerg, F. & Soelberg Sorensen, P. A comparison of multiple sclerosis clinical disease activity between patients treated with natalizumab and fingolimod. Mult. Scler. http://dx.doi.org/10.1177/1352458516643393 (2016).

  99. Spelman, T. et al. Risk of early relapse following the switch from injectables to oral agents for multiple sclerosis. Eur. J. Neurol. 23, 729–736 (2016).

    Article  CAS  PubMed  Google Scholar 

  100. Bloomgren, G. et al. Risk of natalizumab-associated progressive multifocal leukoencephalopathy. N. Engl. J. Med. 366, 1870–1880 (2012).

    Article  CAS  PubMed  Google Scholar 

  101. O'Connor, P. W. et al. Disease activity return during natalizumab treatment interruption in patients with multiple sclerosis. Neurology 76, 1858–1865 (2011).

    Article  CAS  PubMed  Google Scholar 

  102. Cohen, M. et al. Switching from natalizumab to fingolimod in multiple sclerosis: a French prospective study. JAMA Neurol. 71, 436–441 (2014).

    Article  PubMed  Google Scholar 

  103. Iaffaldano, P. et al. Fingolimod versus interferon beta/glatiramer acetate after natalizumab suspension in multiple sclerosis. Brain 138, 3275–3286 (2015). The first comparative study demonstrating the superiority of fingolimod versus BRACE therapy in controlling diseases reactivation after natalizumab suspension in a real-world context.

    Article  PubMed  Google Scholar 

  104. Sorensen, P. S. et al. Recurrence or rebound of clinical relapses after discontinuation of natalizumab therapy in highly active MS patients. J. Neurol. 261, 1170–1177 (2014).

    Article  CAS  PubMed  Google Scholar 

  105. Clerico, M. et al. Treatment of relapsing–remitting multiple sclerosis after 24 doses of natalizumab: evidence from an Italian spontaneous, prospective, and observational study (the TY-STOP Study). JAMA Neurol. 71, 954–960 (2014).

    Article  PubMed  Google Scholar 

  106. Jokubaitis, V. G. et al. Fingolimod after natalizumab and the risk of short-term relapse. Neurology 82, 1204–1211 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Alping, P. et al. Rituximab versus fingolimod after natalizumab in multiple sclerosis patients. Ann. Neurol. 79, 950–958 (2016).

    Article  CAS  PubMed  Google Scholar 

  108. Parsons, L. S. Reducing bias in a propensity score matched pair sample using greedy matching techniques. SAS http://www2.sas.com/proceedings/sugi26/p214-26.pdf (2001).

  109. Trojano, M. et al. New natural history of interferon-beta-treated relapsing multiple sclerosis. Ann. Neurol. 61, 300–306 (2007). The first study addressing the issue of long-term effectiveness of IFN-β treatment in MS by using propensity score technique.

    Article  CAS  PubMed  Google Scholar 

  110. Lunceford, J. K. & Davidian, M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat. Med. 23, 2937–2960 (2004).

    Article  PubMed  Google Scholar 

  111. Bergamaschi, R. et al. Immunomodulatory therapies delay disease progression in multiple sclerosis. Mult. Scler. 22, 1732–1740 (2016).

    Article  PubMed  Google Scholar 

  112. Gilks, R. & Berzuini, C. Following a moving target — Monte Carlo inference for dynamic Bayesian models. J. R. Stat. Soc. B 63, 127–146 (2001).

    Article  Google Scholar 

  113. Tedeholm, H. et al. Time to secondary progression in patients with multiple sclerosis treated with first generation immunomodulating drugs. Mult. Scler. 19, 765–774 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Shirani, A. et al. Association between use of interferon beta and progression of disability in patients with relapsing–remitting multiple sclerosis. JAMA 308, 247–256 (2012).

    CAS  PubMed  Google Scholar 

  115. Palace, J. et al. Effectiveness and cost-effectiveness of interferon beta and glatiramer acetate in the UK Multiple Sclerosis Risk Sharing Scheme at 6 years: a clinical cohort study with natural history comparator. Lancet Neurol. 14, 497–505 (2015). The first study assessing cost–utility ratios and cost-effectiveness in patients with MS treated with BRACE therapies over a 6-year period.

    Article  PubMed  Google Scholar 

  116. Craig, B. A. & Sendi, P. P. Estimation of the transition matrix of a discrete-time Markov chain. Health Econ. 11, 33–42 (2002).

    Article  PubMed  Google Scholar 

  117. Jackson, C. H., Sharples, L. S., Thompson, S. G. & Couto, E. Multistate Markov models for disease progression with classification error. J. R. Stat. Soc. D 52, 193–209 (2003).

    Article  Google Scholar 

  118. Kalincik, T. et al. Defining reliable disability outcomes in multiple sclerosis. Brain 138, 3287–3298 (2015).

    Article  PubMed  Google Scholar 

  119. Lorscheider, J. et al. Defining secondary progressive multiple sclerosis. Brain 139, 2395–2405 (2016).

    Article  PubMed  Google Scholar 

  120. Ziemssen, T., Kern, R. & Cornelissen, C. The PANGAEA study design — a prospective, multicenter, non-interventional, long-term study on fingolimod for the treatment of multiple sclerosis in daily practice. BMC Neurol. 15, 93 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Linker, R. A. & Wendt, G. Cardiac safety profile of first dose of fingolimod for relapsing–remitting multiple sclerosis in real-world settings: data from a German prospective multi-center observational study. Neurol. Ther. http://dx.doi.org/10.1007/s40120-016-0051-7 (2016).

  122. Miclea, A. et al. Safety and efficacy of dimethyl fumarate in multiple sclerosis: a multi-center observational study. J. Neurol. 263, 1626–1632 (2016).

    Article  CAS  PubMed  Google Scholar 

  123. Frisell, T. et al. Comparative analysis of first-year fingolimod and natalizumab drug discontinuation among Swedish patients with multiple sclerosis. Mult. Scler. 22, 85–93 (2016).

    Article  CAS  PubMed  Google Scholar 

  124. Butzkueven, H. et al. Efficacy and safety of natalizumab in multiple sclerosis: interim observational programme results. J. Neurol. Neurosurg. Psychiatry 85, 1190–1197 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  125. Zhang, T. et al. Examining the effects of comorbidities on disease-modifying therapy use in multiple sclerosis. Neurology 86, 1287–1295 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  126. Issa, N. T., Byers, S. W. & Dakshanamurthy, S. Big data: the next frontier for innovation in therapeutics and healthcare. Expert Rev. Clin. Pharmacol. 7, 293–298 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Thorpe, K. E. et al. A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. J. Clin. Epidemiol. 62, 464–475 (2009).

    Article  PubMed  Google Scholar 

  128. van Staa, T. P. et al. The opportunities and challenges of pragmatic point-of-care randomised trials using routinely collected electronic records: evaluations of two exemplar trials. Health Technol. Assess. 18, 1–146 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  129. Fiore, L. D. & Lavori, P. W. Integrating randomized comparative effectiveness research with patient care. N. Engl. J. Med. 374, 2152–2158 (2016).

    Article  CAS  PubMed  Google Scholar 

  130. Patsopoulos, N. A. A pragmatic view on pragmatic trials. Dialogues Clin. Neurosci. 13, 217–224 (2011).

    PubMed  PubMed Central  Google Scholar 

  131. Saaga, K. G. et al. Improving the efficiency and effectiveness of pragmatic clinical trials in older adults in the United States. Contemp. Clin. Trials 33, 1211–1216 (2012).

    Article  Google Scholar 

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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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  4. Jan Hillert
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  7. Tim Spelman
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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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