LEADER 03802nam 22005655 450 001 9910349444503321 005 20200702220551.0 010 $a3-030-19918-5 024 7 $a10.1007/978-3-030-19918-0 035 $a(CKB)4100000009191104 035 $a(DE-He213)978-3-030-19918-0 035 $a(MiAaPQ)EBC5928083 035 $a(PPN)258875798 035 $a(EXLCZ)994100000009191104 100 $a20190903d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEfficacy Analysis in Clinical Trials an Update $eEfficacy Analysis in an Era of Machine Learning /$fby Ton J. Cleophas, Aeilko H. Zwinderman 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XI, 304 p. 295 illus., 44 illus. in color.) 311 $a3-030-19917-7 327 $aPreface -- 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. 330 $aMachine 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. 606 $aMedicine 606 $aStatistics 606 $aBiometry 606 $aBiomedicine, general$3https://scigraph.springernature.com/ontologies/product-market-codes/B0000X 606 $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 606 $aBiostatistics$3https://scigraph.springernature.com/ontologies/product-market-codes/L15020 615 0$aMedicine. 615 0$aStatistics. 615 0$aBiometry. 615 14$aBiomedicine, general. 615 24$aStatistics for Life Sciences, Medicine, Health Sciences. 615 24$aBiostatistics. 676 $a006.31 676 $a615.50724 700 $aCleophas$b Ton J$4aut$4http://id.loc.gov/vocabulary/relators/aut$0472359 702 $aZwinderman$b Aeilko H$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910349444503321 996 $aEfficacy Analysis in Clinical Trials an Update$92115707 997 $aUNINA