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03773nam a2200493 4500 |
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978-3-030-19918-0 |
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20191024212624.0 |
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190903s2019 gw | s |||| 0|eng d |
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|a 9783030199180
|9 978-3-030-19918-0
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|a 10.1007/978-3-030-19918-0
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|a Cleophas, Ton J.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Efficacy Analysis in Clinical Trials an Update
|h [electronic resource] :
|b Efficacy Analysis in an Era of Machine Learning /
|c by Ton J. Cleophas, Aeilko H. Zwinderman.
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|a 1st ed. 2019.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2019.
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|a XI, 304 p. 295 illus., 44 illus. in color.
|b online resource.
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|a text
|b txt
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|a computer
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|a online resource
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|a text file
|b PDF
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|a 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.
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|a 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.
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|a Medicine.
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|a Statistics .
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|a Biostatistics.
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|a Biomedicine, general.
|0 http://scigraph.springernature.com/things/product-market-codes/B0000X
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|a Statistics for Life Sciences, Medicine, Health Sciences.
|0 http://scigraph.springernature.com/things/product-market-codes/S17030
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|a Biostatistics.
|0 http://scigraph.springernature.com/things/product-market-codes/L15020
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|a Zwinderman, Aeilko H.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783030199173
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|i Printed edition:
|z 9783030199197
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|i Printed edition:
|z 9783030199203
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|u https://doi.org/10.1007/978-3-030-19918-0
|z Full Text via HEAL-Link
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|a ZDB-2-SBL
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|a Biomedical and Life Sciences (Springer-11642)
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