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Emerging opportunities to harness real world data: An introduction to data sources, concepts, and applications
Author(s) -
O'Leary Colin P.,
Cavender Matthew A.
Publication year - 2020
Publication title -
diabetes, obesity and metabolism
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.445
H-Index - 128
eISSN - 1463-1326
pISSN - 1462-8902
DOI - 10.1111/dom.13948
Subject(s) - observational study , randomized controlled trial , quality (philosophy) , computer science , clinical study design , clinical trial , data quality , health care , data science , real world evidence , risk analysis (engineering) , comparative effectiveness research , medicine , medical physics , alternative medicine , operations management , engineering , surgery , metric (unit) , epistemology , pathology , economics , economic growth , philosophy
Abstract While randomized controlled trials (RCTs) are the gold standard for comparative effectiveness research, they are unable to provide the answers to all pertinent clinical and research questions. Real world evidence (RWE), that is, clinical evidence obtained outside RCTs and often through routine clinical practice, offers the potential to conduct observational studies that accelerate advances in care, improve outcomes for patients, and provide important insights that can answer important questions. Once appropriate information technology is available, real world data can be cost‐effective to generate. RWE serves a vital role in the evaluation of treatment strategies for which there are no RCTs and for describing patterns of care. RWE also serves as an important adjunct to RCTs and can be used to determine if benefits seen in RCTs extend to clinical practice, provide insight into the findings of RCTs, generate hypotheses for future RCTs, and inform the design of future RCTs. These potential benefits must be balanced against some of the important limitations of RWE, including variable data quality, lack of granularity for important clinical variables, and the potential for bias and confounding. By using appropriate analytic techniques and study design, these limitations can be minimized but not eliminated. Going forward, RWE studies may be enhanced by using rigorous data quality standards, incorporating randomization, developing more prospective registries, and better leveraging data from electronic health records.