New opportunities of real-world data from clinical routine settings in life-cycle management of drugs: example of an integrative approach in multiple sclerosis

References

• Amery WK, ISPE. Why there is a need for pharmacovigilance. Pharmacoepidemiol Drug Saf 1999;8:61-4
   PubMed Web of Science ®Google Scholar
• Strom BL. Sample size considerations for pharmacoepidemiology studies. In: Strom BL, Kimmel SE, Hennessey S (eds.) Pharmacoepidemiology, 5th ed. Sussex, England: John Willey & Sons; 2012:52-61
   Google Scholar
• World Health Organization. Quality Assurance and Safety of Medicines Team. The safety of medicines in public health programmes?: pharmacovigilance, an essential tool. Geneva: World Health Organization, 2006. Available at: http://www.who.int/iris/handle/10665/43384 [Last accessed 23 March 2015]
   Google Scholar
• Rothwell PM. External validity of randomised controlled trials: ‘to whom do the results of this trial apply?’ Lancet 2005;365:82-93
   PubMed Web of Science ®Google Scholar
• Schneeweiss S, Solomon DH. Not-so-surprising findings. Can Med Assoc J 2006;175:171-2; author reply 172
   Web of Science ®Google Scholar
• Thorpe KE, Zwarenstein M, Oxman AD, et al. A pragmatic–explanatory continuum indicator summary (PRECIS): a tool to help trial designers. J Clin Epidemiol 2009;62:464-75
   PubMed Web of Science ®Google Scholar
• Eichler H-G, Abadie E, Breckenridge A, et al. Bridging the efficacy–effectiveness gap: a regulator’s perspective on addressing variability of drug response. Nat Rev Drug Discov 2011;10:495-506
   PubMed Web of Science ®Google Scholar
• Committee for Medicinal Products for Human Use (CHMP). Guideline on risk management systems for medicinal products for human use. London: European Medicines Agency, 2005. (Doc. Ref. EMEA/CHMP/96268/2005)
   Google Scholar
• González-Rubio F, Calderón-Larrañaga A, Poblador-Plou B, et al. Underreporting of recognized adverse drug reactions by primary care physicians: an exploratory study. Pharmacoepidemiol Drug Saf 2011;20:1287-94
   PubMed Web of Science ®Google Scholar
• Eichler H-G, Bloechl-Daum B, Brasseur D, et al. The risks of risk aversion in drug regulation. Nat Rev Drug Discov 2013;12:907-16
   PubMed Web of Science ®Google Scholar
• Observational Medical Outcome Project. Available at: http://omop.org/. [Last accessed 23 March 2015]
   Google Scholar
• Innovative Medicines Initiative. Available at: http://www.imi-getreal.eu/. [Last accessed 23 March 2015]
   Google Scholar
• Garrison LP Jr, Neumann PJ, Erickson P, et al. Using real-world data for coverage and payment decisions: the ISPOR Real-World Data Task Force report. Value Health J Int Soc Pharmacoeconomics Outcomes Res 2007;10:326-35
   PubMed Web of Science ®Google Scholar
• Sospedra M, Martin R. Immunology of multiple sclerosis. Annu Rev Immunol 2005;23:683-747
   PubMed Web of Science ®Google Scholar
• Frohman EM, Racke MK, Raine CS. Multiple sclerosis – the plaque and its pathogenesis. N Engl J Med 2006;354:942-55
   PubMed Web of Science ®Google Scholar
• Niedziela N, Adamczyk-Sowa M, Pierzchała K. Epidemiology and clinical record of multiple sclerosis in selected countries: a systematic review. Int J Neurosci 2014;124:322-30
   Google Scholar
• Lublin FD, Baier M, Cutter G. Effect of relapses on development of residual deficit in multiple sclerosis. Neurology 2003;61:1528-32
   PubMed Web of Science ®Google Scholar
• Killestein J, Rudick RA, Polman CH. Oral treatment for multiple sclerosis. Lancet Neurol 2011;10:1026-34
   PubMed Web of Science ®Google Scholar
• Derwenskus J. Current disease-modifying treatment of multiple sclerosis. Mt Sinai J Med NY 2011;78:161-75
   PubMed Web of Science ®Google Scholar
• Reynolds MW, Stephen R, Seaman C, Rajagopalan K. Healthcare resource utilization following switch or discontinuation in multiple sclerosis patients on disease modifying drugs. J Med Econ 2010;13:90-8
   PubMedGoogle Scholar
• Clerico M, Barbero P, Contessa G, et al. Adherence to interferon-beta treatment and results of therapy switching. J Neurol Sci 2007;259:104-8
   PubMed Web of Science ®Google Scholar
• Río J, Porcel J, Téllez N, et al. Factors related with treatment adherence to interferon beta and glatiramer acetate therapy in multiple sclerosis. Mult Scler Houndmills Basingstoke Engl 2005;11:306-9
   PubMed Web of Science ®Google Scholar
• Dreyer NA. Making observational studies count: shaping the future of comparative effectiveness research. Epidemiol Camb Mass 2011;22:295-7
   PubMed Web of Science ®Google Scholar
• Schneeweiss S, Gagne JJ, Glynn RJ, et al. Assessing the comparative effectiveness of newly marketed medications: methodological challenges and implications for drug development. Clin Pharmacol Ther 2011;90:777-90
   PubMed Web of Science ®Google Scholar
• Gliklich RE, Dreyer NA (eds.) Registries for Evaluating Patient Outcomes: A User’s Guide, 2nd edn. Rockville (MD): Agency for Healthcare Research and Quality (US), 2010
   Google Scholar
• Drolet BC, Johnson KB. Categorizing the world of registries. J Biomed Inform 2008;41:1009-20
   PubMed Web of Science ®Google Scholar
• Boström I, Stawiarz L, Landtblom A-M. Sex ratio of multiple sclerosis in the National Swedish MS Register (SMSreg). Mult Scler Houndmills Basingstoke Engl 2013;19:46-52
   PubMed Web of Science ®Google Scholar
• Koch-Henriksen N, Rasmussen S, Stenager E, Madsen M. The Danish Multiple Sclerosis Registry. History, data collection and validity. Dan Med Bull 2001;48:91-4
   PubMed Web of Science ®Google Scholar
• Myhr K-M, Grytten N, Aarseth JH, Nyland H. The Norwegian Multiple Sclerosis National Competence Centre and National Multiple Sclerosis registry – a resource for clinical practice and research. Acta Neurol Scand Suppl 2006;183:37-40
   PubMedGoogle Scholar
• Flachenecker P, Stuke K, Elias W, et al. Multiple sclerosis registry in Germany: results of the extension phase 2005/2006. Dtsch Ärztebl Int 2008;105:113-19
   PubMed Web of Science ®Google Scholar
• Trojano M, Paolicelli D, Lepore V, et al.; Italian MSDN Group. Italian Multiple Sclerosis Database Network. Neurol Sci Off J Ital Neurol Soc Ital Soc Clin Neurophysiol 2006;27(Suppl 5):S358-61
   Google Scholar
• Pugliatti M, Eskic D, Mikolcić T, et al.; EUReMS Consortium. Assess, compare and enhance the status of persons with multiple sclerosis (MS) in Europe: a European register for MS. Acta Neurol Scand Suppl 2012;159:24-30
   Google Scholar
• Butzkueven H, Chapman J, Cristiano E, et al. MSBase: an international, online registry and platform for collaborative outcomes research in multiple sclerosis. Mult Scler Houndmills Basingstoke Engl 2006;12:769-74
   PubMed Web of Science ®Google Scholar
• Marrie RA, Cutter G, Tyry T, et al. Validation of the NARCOMS registry: diagnosis. Mult Scler Houndmills Basingstoke Engl 2007;13:770-5
   PubMed Web of Science ®Google Scholar
• Magalhaes S, Wolfson C. Harmonization: a methodology for advancing research in multiple sclerosis. Acta Neurol Scand Suppl 2012;159:31-5
   Google Scholar
• Flachenecker P, Stuke K. National MS registries. J Neurol 2008;255(Suppl 6):102-8
   PubMedGoogle Scholar
• Lauer MS, D’Agostino RB. The randomized registry trial – the next disruptive technology in clinical research? N Engl J Med 2013;369:1579-81
   PubMed Web of Science ®Google Scholar
• Fröbert O, Lagerqvist B, Olivecrona GK, et al. TASTE Trial: thrombus aspiration during ST-segment elevation myocardial infarction. N Engl J Med 2013;369:1587-97
   PubMed Web of Science ®Google Scholar
• Schneeweiss S, Avorn J. A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol 2005;58:323-37
   PubMed Web of Science ®Google Scholar
• Suissa S, Garbe E. Primer: administrative health databases in observational studies of drug effects – advantages and disadvantages. Nat Clin Pract Rheumatol 2007;3:725-32
   PubMedGoogle Scholar
• Xia Z, Secor E, Chibnik LB, et al. Modeling disease severity in multiple sclerosis using electronic health records. PloS One 2013;8:e78927
   PubMed Web of Science ®Google Scholar
• Davis MF, Sriram S, Bush WS, et al. Automated extraction of clinical traits of multiple sclerosis in electronic medical records. J Am Med Inform Assoc 2013;20:e334-40
   PubMed Web of Science ®Google Scholar
• Chastek BJ, Oleen-Burkey M, Lopez-Bresnahan MV. Medical chart validation of an algorithm for identifying multiple sclerosis relapse in healthcare claims. J Med Econ 2010;13:618-25
   PubMedGoogle Scholar
• Ollendorf DA, Jilinskaia E, Oleen-Burkey M. Clinical and economic impact of glatiramer acetate versus beta interferon therapy among patients with multiple sclerosis in a managed care population. J Manag Care Pharm 2002;8:469-76
   PubMedGoogle Scholar
• Herman RA, Gilchrist B, Link BK, Carnahan R. A systematic review of validated methods for identifying lymphoma using administrative data. Pharmacoepidemiol Drug Saf 2012;21(Suppl 1):203-12
   PubMedGoogle Scholar
• Velentgas P, Dreyer NA, Nourjah P, et al. (eds.) Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide. AHRQ Publication No. 12(13)-EHC099. Rockville, MD: Agency for Healthcare Research and Quality; January 2013. Available at: www.effectivehealthcare.ahrq.gov/Methods-OCER.cfm [Last accessed 23 March 2015]
   Google Scholar
• Dreyer NA, Schneeweiss S, McNeil BJ, et al.; GRACE Initiative. GRACE principles: recognizing high-quality observational studies of comparative effectiveness. Am J Manag Care 2010;16:467-71
   PubMed Web of Science ®Google Scholar
• ISPE. Guidelines for good pharmacoepidemiology practices (GPP). Pharmacoepidemiol Drug Saf 2008;17:200-8
   PubMed Web of Science ®Google Scholar
• Von Elm E, Altman DG, Egger M, et al. STROBE initiative: the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med 2007;147:573-7
   PubMed Web of Science ®Google Scholar
• The European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP). Guide on Methodological Standards in Pharmacoepidemiology (Revision 3). EMA/95098/2010. Available at: http://www.encepp.eu/standards_and_guidance [Last accessed 23 March 2015]
   Google Scholar
• Stürmer T, Jonsson Funk M, Poole C, Brookhart MA. Nonexperimental comparative effectiveness research using linked healthcare databases. Epidemiol Camb Mass 2011;22:298-301
   PubMed Web of Science ®Google Scholar
• Ray WA. Evaluating medication effects outside of clinical trials: new-user designs. Am J Epidemiol 2003;158:915-20
   PubMed Web of Science ®Google Scholar
• Seeger JD, Williams PL, Walker AM. An application of propensity score matching using claims data. Pharmacoepidemiol Drug Saf 2005;14:465-76
   PubMed Web of Science ®Google Scholar
• Austin PC. The performance of different propensity score methods for estimating marginal odds ratios. Stat Med 2007;26:3078-94
   PubMed Web of Science ®Google Scholar
• Glynn RJ, Schneeweiss S, Stürmer T. Indications for propensity scores and review of their use in pharmacoepidemiology. Basic Clin Pharmacol Toxicol 2006;98:253-9
   PubMed Web of Science ®Google Scholar
• Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiol Camb Mass 2000;11:550-60
   PubMed Web of Science ®Google Scholar
• Danaei G, Rodríguez LAG, Cantero OF, et al. Observational data for comparative effectiveness research: an emulation of randomised trials of statins and primary prevention of coronary heart disease. Stat Methods Med Res 2013;22:70-96
   PubMed Web of Science ®Google Scholar
• Bergvall N, Makin C, Lahoz R, et al. Relapse rates in patients with multiple sclerosis switching from interferon to fingolimod or glatiramer acetate: a US claims database study. PloS One 2014;9:e88472
   PubMed Web of Science ®Google Scholar
• Bergvall N, Makin C, Lahoz R, et al. Comparative effectiveness of fingolimod versus interferons or glatiramer acetate for relapse rates in multiple sclerosis: a retrospective US claims database analysis. Curr Med Res Opin 2013;29:1647-56
   PubMed Web of Science ®Google Scholar
• Spelman T, Bergvall N, Tomic D, et al. Real world comparative effectiveness of fingolimod and interferon/glatiramer therapies in a switch population using propensity-matched data from MSBase (abstract P1096). Presented at the 29th Congress of the European Committee for Treatment and Research in Multiple Sclerosis, Copenhagen, Denmark, October 2--5; 2013
   Google Scholar
• Schneeweiss S. Learning from big health care data. N Engl J Med 2014;370:2161-3
   PubMed Web of Science ®Google Scholar
• Kaizar EE. Estimating treatment effect via simple cross design synthesis. Stat Med 2011;30:2986-3009
   PubMed Web of Science ®Google Scholar
• Hand DJ, Mannila H, Smyth P. Principles of Data Mining. Cambridge, MA & London: MIT Press, 2001
   Google Scholar