Statistics for Biology and Health

Adaptive Regression for Modeling Nonlinear Relationships

Authors: Knafl, George J., Ding, Kai

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  • Provides insight into modeling of nonlinear relationships and also justifications for when to use them, thereby providing novel insights about relationships
  • Addresses not only adaptive generation of additive models but also of models based on nonlinear interactions
  • Discusses adaptive modeling of variances/dispersions as well as of means
  • Highlights both univariate and multivariate outcomes, rather than solely univariate outcomes                                                                                                                                                                                                                                                                                           
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eBook 59,49 €
price for Spain (gross)
  • ISBN 978-3-319-33946-7
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 72,79 €
price for Spain (gross)
  • ISBN 978-3-319-33944-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Softcover 72,79 €
price for Spain (gross)
  • ISBN 978-3-319-81638-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. 

A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the standard, logistic, and Poisson regression contexts with continuous, discrete, and counts outcomes, respectively, either univariate or multivariate. The book also provides a comparison of adaptive modeling to generalized additive modeling (GAM) and multiple adaptive regression splines (MARS) for univariate outcomes.  

The authors have created customized SAS macros for use in conducting adaptive regression modeling. These macros and code for conducting the analyses discussed in the book are available through the first author's website and online via the book’s Springer website. Detailed descriptions of how to use these macros and interpret their output appear throughout the book. These methods can be implemented using other programs. 


About the authors

George Knafl is Professor and Biostatistician in the School of Nursing of the University of North Carolina at Chapel Hill where he teaches statistics courses to doctoral nursing students, consults with graduate students and faculty on their research, and conducts his own research. He has over 35 years of experience in teaching, consulting, and research in statistics. His research involves development of methods for searching through alternative models for data to identify an effective choice for modeling those data and the application of those methods to the analysis of health science data sets. He is also Professor Emeritus in the College of Computing and Digital Media at DePaul University and has also taught in Schools of Nursing at Yale University and the Oregon Health and Sciences University.

Kai Ding is Assistant Professor, Department of Biostatistics and Epidemiology at the University of Oklahoma (OU) Health Sciences Center. He is also Associated Member of the Peggy and Charles Stephenson Cancer Center (SCC) of OU Medicine. Dr. Ding received his Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill in 2010. His research has focuses on survival analysis and semiparametric inference. He has been involved in the design and analysis of numerous research studies in cancer and ophthalmology and currently serves on the Scientific Review Committee and the Protocol Monitoring Committee of the SCC.                                                                                                                                                                                                                                                                          

Reviews

                                                                                                      
                                                                                                                                          

Table of contents (20 chapters)

  • Introduction

    Knafl, George J. (et al.)

    Pages 1-8

    Preview Buy Chapter 30,19 €
  • Adaptive Regression Modeling of Univariate Continuous Outcomes

    Knafl, George J. (et al.)

    Pages 11-43

    Preview Buy Chapter 30,19 €
  • Adaptive Regression Modeling of Univariate Continuous Outcomes in SAS

    Knafl, George J. (et al.)

    Pages 45-62

    Preview Buy Chapter 30,19 €
  • Adaptive Regression Modeling of Multivariate Continuous Outcomes

    Knafl, George J. (et al.)

    Pages 63-108

    Preview Buy Chapter 30,19 €
  • Adaptive Regression Modeling of Multivariate Continuous Outcomes in SAS

    Knafl, George J. (et al.)

    Pages 109-132

    Preview Buy Chapter 30,19 €

Buy this book

eBook 59,49 €
price for Spain (gross)
  • ISBN 978-3-319-33946-7
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 72,79 €
price for Spain (gross)
  • ISBN 978-3-319-33944-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Softcover 72,79 €
price for Spain (gross)
  • ISBN 978-3-319-81638-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Adaptive Regression for Modeling Nonlinear Relationships
Authors
Series Title
Statistics for Biology and Health
Copyright
2016
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-33946-7
DOI
10.1007/978-3-319-33946-7
Hardcover ISBN
978-3-319-33944-3
Softcover ISBN
978-3-319-81638-8
Series ISSN
1431-8776
Edition Number
1
Number of Pages
XXV, 372
Number of Illustrations
44 b/w illustrations, 13 illustrations in colour
Topics