Quantile regression in clinical research : complete analysis for data at a loss of homogeneity / Ton J. Cleophas, Aeilko H. Zwinderman.
Cleophas, Ton J. M., author.; Zwinderman, Aeilko H., author.
2021
R853.S7 C54 2021
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Title
Quantile regression in clinical research : complete analysis for data at a loss of homogeneity / Ton J. Cleophas, Aeilko H. Zwinderman.
Author
Cleophas, Ton J. M., author.
ISBN
9783030828400 (electronic bk.)
3030828409 (electronic bk.)
9783030828394
3030828395
DOI
https://doi.org/10.1007/978-3-030-82840-0
Published
Cham : Springer, [2021]
Copyright
©2021
Language
English
Description
1 online resource
Item Number
10.1007/978-3-030-82840-0 doi
Call Number
R853.S7 C54 2021
Dewey Decimal Classification
610.727
Summary
Quantile 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. .
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Description based on print version record.
Added Author
Zwinderman, Aeilko H., author.
Available in Other Form
Quantile regression in clinical research.
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Chapter 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.

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