03802nam 22005655 450 991034944450332120200702220551.03-030-19918-510.1007/978-3-030-19918-0(CKB)4100000009191104(DE-He213)978-3-030-19918-0(MiAaPQ)EBC5928083(PPN)258875798(EXLCZ)99410000000919110420190903d2019 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierEfficacy Analysis in Clinical Trials an Update Efficacy Analysis in an Era of Machine Learning /by Ton J. Cleophas, Aeilko H. Zwinderman1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (XI, 304 p. 295 illus., 44 illus. in color.) 3-030-19917-7 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.MedicineStatisticsBiometryBiomedicine, generalhttps://scigraph.springernature.com/ontologies/product-market-codes/B0000XStatistics for Life Sciences, Medicine, Health Scienceshttps://scigraph.springernature.com/ontologies/product-market-codes/S17030Biostatisticshttps://scigraph.springernature.com/ontologies/product-market-codes/L15020Medicine.Statistics.Biometry.Biomedicine, general.Statistics for Life Sciences, Medicine, Health Sciences.Biostatistics.006.31615.50724Cleophas Ton Jauthttp://id.loc.gov/vocabulary/relators/aut472359Zwinderman Aeilko Hauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910349444503321Efficacy Analysis in Clinical Trials an Update2115707UNINA