title: Are Randomized Controlled Trials the (G)old Standard? From Clinical Intelligence to Prescriptive Analytics.
abstract: Despite the accelerating pace of scientific discovery, the current clinical research enterprise does not sufficiently address pressing clinical questions. Given the constraints on clinical trials, for a majority of clinical questions, the only relevant data available to aid in decision making are based on observation and experience. Our purpose here is 3-fold. First, we describe the classic context of medical research guided by Poppers' scientific epistemology of "falsificationism." Second, we discuss challenges and shortcomings of randomized controlled trials and present the potential of observational studies based on big data. Third, we cover several obstacles related to the use of observational (retrospective) data in clinical studies. We conclude that randomized controlled trials are not at risk for extinction, but innovations in statistics, machine learning, and big data analytics may generate a completely new ecosystem for exploration and validation.
authors.affiliation: Department of Anesthesiology, Critical Care, Emergency Medicine, Pain Therapy, Ziekenhuis Oost-Limburg, Genk, Belgium. svanpoucke@gmail.com.
authors.forename: Sven
authors.identifier: 0000-0001-8070-8786
authors.initials: S
authors.lastname: Van Poucke
authors.forename: Michiel
authors.identifier: 0000-0001-5627-0323
authors.initials: M
authors.lastname: Thomeer
authors.forename: John
authors.identifier: 0000-0003-4564-411X
authors.initials: J
authors.lastname: Heath
authors.forename: Milan
authors.identifier: 0000-0002-1631-6531
authors.initials: M
authors.lastname: Vukicevic
mesh_terms: D035843:Biomedical Research / Q000379:methods* / Q000592:standards*; D057225:Data Mining / Q000379:methods*; D003657:Decision Making; D006801:Humans; D007360:Intelligence; D064887:Observational Studies as Topic / Q000379:methods*; D016032:Randomized Controlled Trials as Topic / Q000379:methods*
keywords: algorithm; big data; data mining; ensemble methods; modeling; predictive analytics; randomized controlled trials