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Table 1 Methodologies for collecting real-world data [4] and examples of their application to multiple sclerosis (MS) studies

From: The importance of collecting structured clinical information on multiple sclerosis

Source Explanation Advantages Limitations Examples in MS
RCTs extensions • Supplement trial data • Extend RCT into real world • Short study duration
• Limited patient population
• No information on rare events
• ENDORSE: EQ-5D and SF-36 in patients treated with DMF [10]
Registries • Population-based collection of information • Long-term data natural history and disease management
• Regional comparisons
• Non-randomized design
• Incomparable patient groupings
• Discontinuous visit schedule
• Varying practice patterns
• Inter-regional extrapolation not always appropriate
• Lyons MS database: disability progression [11]
• SMSreg: epidemiology and treatment outcomes [16, 19]
• MSBase: treatment outcomes (69 countries) [1315, 17]
Prospective observational studies • Pre-defined outcome measures in clinical practice • Robust dataset powered to answer specific questions • Hawthorne effect (patients behave differently because they know they are being observed) • PANGAEA, TOP: fingolimod and natalizumab clinical trial [9, 20]
PASSs • Voluntary or imposed by regulatory authorities for approval • Ongoing monitoring of the benefit–risk profile • No obligation for regulatory submission of protocols and study reports for voluntary PASSs • PANGAEA: German voluntary PASS [20]
Administrative data • Data required for reimbursement • Quick, low-cost analyses
• Large patient populations provide information on rare events
• Privacy concerns limit access to data
• Incomplete or inaccurate data
• Costs and charges are not differentiated
• Pharmetrics Plus™ and Medco databases: relapses, treatment compliance, resource use and inpatient stays [68]
Health surveys • Descriptive data • Provide broadly generalizable data • Not product-specific
• Subjective
• Relies on participant recollection
• NARCOMS survey: symptoms, comorbidities and health-related quality of life [18]
EMRs • Real-time data collection • Low cost
• Detailed information over long periods
• High-end statistical analysis tools required • EMRs: diagnosis, disease progression, symptoms and treatment [12]
  1. DMF, Dimethyl fumarate; EMR, Electronic medical record; ENDORSE, BG00012 monotherapy safety and efficacy extension study in MS; EQ-5D, European Quality of Life-5 dimensions questionnaire; MSBase, Multiple Sclerosis dataBase; NARCOMS, North American Research Committee on Multiple Sclerosis; PANGAEA, Post-Authorization Noninterventional German sAfety of GilEnyA in RRMS patients; PASS, Post-authorization safety study; RCT, Randomized controlled trial; SF-36, 36-Item Short Form; SMSreg, Swedish MS registry; TOP, TYSABRI Observational Program