000914596 000__ 03456cam\a2200469Ii\4500 000914596 001__ 914596 000914596 005__ 20251006230155.0 000914596 006__ m\\\\\o\\d\\\\\\\\ 000914596 007__ cr\cn\nnnunnun 000914596 008__ 190918s2019\\\\sz\a\\\\ob\\\\001\0\eng\d 000914596 019__ $$a1121099312$$a1121276563 000914596 020__ $$a9783030199180$$q(electronic book) 000914596 020__ $$a3030199185$$q(electronic book) 000914596 020__ $$z9783030199173 000914596 0247_ $$a10.1007/978-3-030-19918-0$$2doi 000914596 0247_ $$a10.1007/978-3-030-19 000914596 035__ $$aSP(OCoLC)on1119736515 000914596 035__ $$aSP(OCoLC)1119736515$$z(OCoLC)1121099312$$z(OCoLC)1121276563 000914596 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dUKMGB$$dLQU$$dOCLCF$$dEBLCP 000914596 049__ $$aISEA 000914596 050_4 $$aR853.C55$$bC56 2019eb 000914596 08204 $$a615.5072/4$$223 000914596 1001_ $$aCleophas, Ton J. M.,$$eauthor. 000914596 24510 $$aEfficacy analysis in clinical trials an update :$$befficacy analysis in an era of machine learning /$$cTon J. Cleophas, Aeilko H. Zwinderman. 000914596 264_1 $$aCham :$$bSpringer,$$c[2019] 000914596 264_4 $$c©2019 000914596 300__ $$a1 online resource :$$billustrations 000914596 336__ $$atext$$btxt$$2rdacontent 000914596 337__ $$acomputer$$bc$$2rdamedia 000914596 338__ $$aonline resource$$bcr$$2rdacarrier 000914596 504__ $$aIncludes bibliographical references and index. 000914596 5050_ $$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. 000914596 506__ $$aAccess limited to authorized users. 000914596 520__ $$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. 000914596 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 18, 2019). 000914596 650_0 $$aClinical trials. 000914596 650_0 $$aMachine learning. 000914596 7001_ $$aZwinderman, Aeilko H.,$$eauthor. 000914596 852__ $$bebk 000914596 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-19918-0$$zOnline Access$$91397441.1 000914596 909CO $$ooai:library.usi.edu:914596$$pGLOBAL_SET 000914596 980__ $$aEBOOK 000914596 980__ $$aBIB 000914596 982__ $$aEbook 000914596 983__ $$aOnline 000914596 994__ $$a92$$bISE