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Beyond the Randomized Clinical Trial: Innovative Data Science to Close the Pediatric Evidence Gap
Author(s) -
Goulooze Sebastiaan C.,
Zwep Laura B.,
Vogt Julia E.,
Krekels Elke H.J.,
Hankemeier Thomas,
Anker John N.,
Knibbe Catherijne A.J.
Publication year - 2020
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1002/cpt.1744
Subject(s) - randomized controlled trial , medline , real world evidence , evidence based medicine , clinical trial , medicine , medical physics , alternative medicine , political science , pathology , law
Despite the application of advanced statistical and pharmacometric approaches to pediatric trial data, a large pediatric evidence gap still remains. Here, we discuss how to collect more data from children by using real‐world data from electronic health records, mobile applications, wearables, and social media. The large datasets collected with these approaches enable and may demand the use of artificial intelligence and machine learning to allow the data to be analyzed for decision making. Applications of this approach are presented, which include the prediction of future clinical complications, medical image analysis, identification of new pediatric end points and biomarkers, the prediction of treatment nonresponders, and the prediction of placebo‐responders for trial enrichment. Finally, we discuss how to bring machine learning from science to pediatric clinical practice. We conclude that advantage should be taken of the current opportunities offered by innovations in data science and machine learning to close the pediatric evidence gap.