Efficacy analysis in clinical trials an update : efficacy analysis in an era of machine learning /

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Bibliographic Details
Author / Creator:Cleophas, Ton J. M., author.
Imprint:Cham : Springer, [2019]
©2019
Description:1 online resource : illustrations (some color)
Language:English
Subject:Clinical trials.
Machine learning.
Clinical trials.
Machine learning.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11956246
Hidden Bibliographic Details
Other authors / contributors:Zwinderman, Aeilko H., author.
ISBN:9783030199180
3030199185
3030199177
9783030199173
9783030199197
3030199193
9783030199203
3030199207
9783030199173
Digital file characteristics:text file PDF
Notes:Includes bibliographical references and index.
Online resource; title from PDF title page (SpringerLink, viewed September 18, 2019).
Summary: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.
Other form:Printed edition: 9783030199173
Printed edition: 9783030199197
Printed edition: 9783030199203
Standard no.:10.1007/978-3-030-19918-0
10.1007/978-3-030-19