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Efficacy analysis in clinical trials...

Cleophas, Ton J.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Efficacy analysis in clinical trials an update/ by Ton J. Cleophas, Aeilko H. Zwinderman.
其他題名:	efficacy analysis in an era of machine learning /
作者:	Cleophas, Ton J.
其他作者:	Zwinderman, Aeilko H.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xi, 304 p. :ill. (some col.), digital ;24 cm.
內容註:	Preface -- Traditional and Machine-Learning Methods for Efficacy Analysis -- Optimal-Scaling for Efficacy Analysis -- Ratio-Statistic for Efficacy Analysis -- Ratio-Statistic for Efficacy Analysis -- Complex-Samples for Efficacy Analysis -- Bayesian-Networks for Efficacy Analysis -- Evolutionary-Operations for Efficacy Analysis -- Automatic-Newton-Modeling for Efficacy Analysis -- High-Risk-Bins for Efficacy Analysis -- Balanced-Iterative-Reducing-Hierarchy for Efficacy Analysis -- Cluster-Analysis for Efficacy Analysis -- Multidimensional-Scaling for Efficacy Analysis -- Binary Decision-Trees for Efficacy Analysis -- Continuous Decision-Trees for Efficacy Analysis -- Automatic-Data-Mining for Efficacy Analysis -- Support-Vector-Machines for Efficacy Analysis -- Neural-Networks for Efficacy Analysis -- Ensembled-Accuracies for Efficacy Analysis -- Ensembled-Correlations for Efficacy Analysis -- Gamma-Distributions for Efficacy Analysis -- Validation with Big Data, a Big Issue -- Index.
Contained By:	Springer Nature eBook
標題:	Clinical trials. -
電子資源:	https://doi.org/10.1007/978-3-030-19918-0
ISBN:	9783030199180

Efficacy analysis in clinical trials an update = efficacy analysis in an era of machine learning /
Cleophas, Ton J.

Efficacy analysis in clinical trials an updateefficacy analysis in an era of machine learning /[electronic resource] :by Ton J. Cleophas, Aeilko H. Zwinderman. - Cham :Springer International Publishing :2019. - xi, 304 p. :ill. (some col.), digital ;24 cm.

Preface -- Traditional and Machine-Learning Methods for Efficacy Analysis -- Optimal-Scaling for Efficacy Analysis -- Ratio-Statistic for Efficacy Analysis -- Ratio-Statistic for Efficacy Analysis -- Complex-Samples for Efficacy Analysis -- Bayesian-Networks for Efficacy Analysis -- Evolutionary-Operations for Efficacy Analysis -- Automatic-Newton-Modeling for Efficacy Analysis -- High-Risk-Bins for Efficacy Analysis -- Balanced-Iterative-Reducing-Hierarchy for Efficacy Analysis -- Cluster-Analysis for Efficacy Analysis -- Multidimensional-Scaling for Efficacy Analysis -- Binary Decision-Trees for Efficacy Analysis -- Continuous Decision-Trees for Efficacy Analysis -- Automatic-Data-Mining for Efficacy Analysis -- Support-Vector-Machines for Efficacy Analysis -- Neural-Networks for Efficacy Analysis -- Ensembled-Accuracies for Efficacy Analysis -- Ensembled-Correlations for Efficacy Analysis -- Gamma-Distributions for Efficacy Analysis -- Validation with Big Data, a Big Issue -- Index.

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.

ISBN: 9783030199180

Standard No.: 10.1007/978-3-030-19918-0doiSubjects--Topical Terms:

724498
Clinical trials.

LC Class. No.: R853.C55 / C546 2019

Dewey Class. No.: 615.50724

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