05964nam a22004575i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050000700172072001500179072002300194072001400217100008500231245016100316250001800477264006900495300006800564336002600632337002600658338003600684347002400720505183000744520247002574650002905044650002505073650002805098650003105126700009005157710003405247773002605281776003605307776003605343776003605379856004605415912001405461912001405475999001705489978-3-031-72247-9DE-He21320251030114629.0cr nn 008mamaa241220s2024    sz |    s    |||| 0|eng d  a97830317224799978-3-031-72247-97 a10.1007/978-3-031-72247-92doi 4aR1 7aMB2bicssc 7aMED0000002bisacsh 7aMB2thema1 aCleophas, Ton J.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut98924510aApplication of Regularized Regressions to Identify Novel Predictors in Clinical Researchh[electronic resource] /cby Ton J. Cleophas, Aeilko H. Zwinderman.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXIII, 273 p. 337 illus., 305 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 a -- Basic Principles of Regression Analysis.  -- Optimal Scaling, Discretization, and Regularization vs Traditional Linear Regression.  -- Regularized Regression Analysis, Ridge, Lasso, Elastic Net Regression Coefficients.  -- Effect of Predictors on Health Scores, 110 Patients, Traditional vs Regularized Regressions.  -- Effect on Physical strength of Races, 60 Patients, Traditional Regression vs Regularized regressions.  -- Effects of Genetic Polymorphisms on Clinical Outcomes, 250 Patients, Traditional vs Regularized Regressions.  -- Effect of Old Treatment and Age on New Treatment, 35 Patients, Traditional vs Regularized Regressions.  -- Effect on Paroxysmal Atrial Fibrillations of Four Predictors, 50 Patients, Traditional vs Regularized Regressions.  -- Effect of Air Quality of Operating Rooms on Infections, 8 Operating Rooms, Traditional vs Regularized Regressions.  -- Effect on Weightloss of Age, Calorieintake, Exercise, Interaction, 64 Patients, Traditional vs Regularized Regressions.  -- Effect on Body Surface Measured of Gender, Age, Weight, Height, and Weight x Height Interaction, 90 Patients, Traditional vs Regularized Regressions.  -- Effect on Paroxysmal Atrial Fibrillations of Gender, Treatment and Their Interaction, 40 Patients, Traditional vs Regularized Regressions.  -- Effect on Hours of Sleep of Treatment Group, Age, Gender, Comorbidity, 20 Patients, Traditional vs Regularized Regressions.  -- Effect of Betaagonist and Prednisone on Peak Expiratory Flow, 78 COPD Patients, Traditional vs Regularized Regressions.  -- Effect on LDL Cholesterol Reduction of Five Predictors, 953 Patients, Traditional vs Regularized Regressions.  -- Effect of Five Factors on Body Weight, 217 Patients, Traditional vs Regularized Regressions.  -- Functional Data Analysis and Regularized Regressions.  aThis textbook is an important novel menu for multiple variables regression entitled "regularized regression". It is a must have for identifying unidentified leading factors. Also, you get fitted parameters for your overfitted data. Finally, there is no more need for commonly misunderstood p-values. Instead, the regression coefficient, R-value, as reported from a regression line has been applied as the key predictive estimator of the regression study. With simple one by one variable regression it is no wider than -1 to +1. With multiple variables regression it can easily get > +1 or < -1. This means we have a seriously flawed regression model, mostly due to collinearity or non-linear data. Completing the analysis will lead to overfitting, and thus a meaningless significant study due to data spread wider than compatible with random. In order for the regression coefficients to remain in the right size, fortunately a shrinking procedure has been invented. In the past two decades regularized regression has become a major topic of research, particularly with high dimensional data. Yet, the method is pretty new and infrequently used in real-data analysis. Its performance as compared to traditional null hypothesis testing has to be confirmed by prospective comparisons. Most studies published to date are of a theoretical nature involving statistical modeling and simulation studies. The journals Nature and Science published 19 and 10 papers of this sort in the past 8 years. The current edition will for the first time systematically test regularized regression against traditional regression analysis in 20 clinical data examples. The edition is also a textbook and tutorial for medical and healthcare students as well as recollection bench and help desk for professionals. Each chapter can be studied as a standalone, and, using, real as well as hypothesized data, it tests the performance of the novel methodology against traditional regressions. Step by step analyses of 20 data files are included for self-assessment. The authors are well qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics and Professor Cleophas is past-president of the American College of Angiology. The authors have been working together for 25 years and their research can be characterized as a continued effort to demonstrate that clinical data analysis is a discipline at the interface of biology and mathematics. 0aMedical sciences.965984 0aStatistics .96598214aHealth Sciences.96598724aApplied Statistics.9659861 aZwinderman, Aeilko H.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9892462 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303172246208iPrinted edition:z978303172248608iPrinted edition:z978303172249340uhttps://doi.org/10.1007/978-3-031-72247-9  aZDB-2-SME  aZDB-2-SXM  c65880d65880