Efficacy Analysis in Clinical Trials an Update Efficacy Analysis in an Era of Machine Learning /

• Κύριοι συγγραφείς: Cleophas, Ton J. (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Zwinderman, Aeilko H. (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Θέματα: Medicine.; Statistics .; Biostatistics.; Biomedicine, general.; Statistics for Life Sciences, Medicine, Health Sciences.
• Φυσική περιγραφή: XI, 304 p. 295 illus., 44 illus. in color. online resource.
• ISBN: 9783030199180
• DOI: 10.1007/978-3-030-19918-0

Machine learning and big data is hot. It is, however, virtually unused in clinical trials. This is so, because randomization is applied to even out multiple variables. Modern medical computer files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This is the first publication of clinical trials that have been systematically analyzed with machine learning. In addition, all of the machine learning analyses were tested against traditional analyses. Step by step statistics for self-assessments are included. The authors conclude, that machine learning is often more informative, and provides better sensitivities of testing than traditional analytic methods do.