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001441742 001__ 1441742
001441742 003__ OCoLC
001441742 005__ 20230309003341.0
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001441742 020__ $$a9783030828400$$q(electronic bk.)
001441742 020__ $$a3030828409$$q(electronic bk.)
001441742 020__ $$z9783030828394
001441742 020__ $$z3030828395
001441742 0247_ $$a10.1007/978-3-030-82840-0$$2doi
001441742 035__ $$aSP(OCoLC)1293295080
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001441742 049__ $$aISEA
001441742 050_4 $$aR853.S7$$bC54 2021
001441742 08204 $$a610.727$$223
001441742 1001_ $$aCleophas, Ton J. M.,$$eauthor.
001441742 24510 $$aQuantile regression in clinical research :$$bcomplete analysis for data at a loss of homogeneity /$$cTon J. Cleophas, Aeilko H. Zwinderman.
001441742 264_1 $$aCham :$$bSpringer,$$c[2021]
001441742 264_4 $$c©2021
001441742 300__ $$a1 online resource
001441742 336__ $$atext$$btxt$$2rdacontent
001441742 337__ $$acomputer$$bc$$2rdamedia
001441742 338__ $$aonline resource$$bcr$$2rdacarrier
001441742 504__ $$aIncludes bibliographical references and index.
001441742 5050_ $$aChapter 1. General Introduction -- Chapter 2. Mathematical Models for Separating Quantiles from One Another -- Part I: Simple Univariate Regressions versus Quantile -- Chapter 3. Traditional and Robust Regressions versus Quantile -- Chapter 4. Autoregressions versus quantile -- Chapter 5. Discrete Trend Analysis versus Quantile -- Chapter 6. Continuous Trend Analysis versus Quantile -- Binary Poisson / Negative Binomial Regression versus Quantile -- Chapter 8. Robust Standard Errors Regressions versus Quantile -- Chapter 9. Optimal Scaling versus Quantile Regression -- Chapter 10. Intercept only Poisson Regression versus Quantile -- Part II: Multiple Variables Regressions versus Quantile -- Chapter 11. Four Predictors Regressions versus Quantile -- Chapter 12. Gene Expressions Regressions, Traditional versus Quantile -- Chapter 13. Koenker's Multiple Variables Regression with Quantile -- Chapter 14. Interaction Adjusted Regression versus Quantile -- Chapter 15. Quantile Regression to Study Corona Deaths -- Chapter 16. Laboratory Values Predict Survival Sepsis, Traditional Regression versus Quantile -- Chapter 17. Multinomial Poisson Regression versus Quantile -- Chapter 18. Regressions with Inconstant Variability versus Quantile -- Chapter 19. Restructuring Categories into Multiple Dummy Variables versus Quantile -- Chapter 20. Poisson Events per Person per Period of Time versus Quantile -- Part III: Special Regressions versus Quantile -- Chapter 21. Two Stage Least Squares Regressions versus Quantile -- Chapter 22. Partial Correlations versus Quantile Regressions -- Chapter 23. Random Intercept Regression versus Quantile -- Chapter 24. Regression Trees versus Quantile -- Chapter 25. Kernel Regression versus Quantile -- Chapter 26. Quasi-likelihood Regression versus Quantile -- Chapter 27. Summaries.
001441742 506__ $$aAccess limited to authorized users.
001441742 520__ $$aQuantile regression is an approach to data at a loss of homogeneity, for example (1) data with outliers, (2) skewed data like corona - deaths data, (3) data with inconstant variability, (4) big data. In clinical research many examples can be given like circadian phenomena, and diseases where spreading may be dependent on subsets with frailty, low weight, low hygiene, and many forms of lack of healthiness. Stratified analyses is the laborious and rather explorative way of analysis, but quantile analysis is a more fruitful, faster and completer alternative for the purpose. Considering all of this, we are on the verge of a revolution in data analysis. The current edition is the first textbook and tutorial of quantile regressions for medical and healthcare students as well as recollection/update bench, and help desk for professionals. Each chapter can be studied as a standalone and covers one of the many fields in the fast growing world of quantile regressions. Step by step analyses of over 20 data files stored at extras.springer.com are included for self-assessment. We should add that the authors are well qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics (2012-2015) and Professor Cleophas is past-president of the American College of Angiology(2000-2002). From their expertise they should be able to make adequate selections of modern quantile regression methods for the benefit of physicians, students, and investigators. .
001441742 588__ $$aDescription based on print version record.
001441742 650_0 $$aClinical medicine$$xResearch$$xStatistical methods.
001441742 650_0 $$aQuantile regression.
001441742 650_6 $$aMédecine clinique$$xRecherche$$xMéthodes statistiques.
001441742 650_6 $$aRégression quantile.
001441742 655_0 $$aElectronic books.
001441742 7001_ $$aZwinderman, Aeilko H.,$$eauthor.
001441742 77608 $$iPrint version:$$aCleophas, Ton J. M.$$tQuantile regression in clinical research.$$dCham : Springer, 2022$$z9783030828394$$w(OCoLC)1295107102
001441742 852__ $$bebk
001441742 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-82840-0$$zOnline Access$$91397441.1
001441742 909CO $$ooai:library.usi.edu:1441742$$pGLOBAL_SET
001441742 980__ $$aBIB
001441742 980__ $$aEBOOK
001441742 982__ $$aEbook
001441742 983__ $$aOnline
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