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Adaptive Regression for Modeling Nonlinear RelationshipsAdaptive Poisson Regression Modeling of Univariate Count Outcomes in SAS

Adaptive Regression for Modeling Nonlinear Relationships: Adaptive Poisson Regression Modeling of... [This chapter describes how to use the genreg macro for adaptive Poisson regression modeling as described in Chap. 12 and its generated output in the special case of univariate count outcomes, possibly converted to rate outcomes through offsets. Example analyses are provided for modeling means and dispersions for non-melanoma skin cancer rates for women of varying ages residing in St. Paul, Minnesota and Fort Worth, Texas, addressing how these rates depend on age and location of residence. One of these analyses provides an example for which adaptive modeling distinctly outperforms recommended degree 1 and 2 fractional polynomials.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Adaptive Regression for Modeling Nonlinear RelationshipsAdaptive Poisson Regression Modeling of Univariate Count Outcomes in SAS

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Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2016
ISBN
978-3-319-33944-3
Pages
265 –274
DOI
10.1007/978-3-319-33946-7_13
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter describes how to use the genreg macro for adaptive Poisson regression modeling as described in Chap. 12 and its generated output in the special case of univariate count outcomes, possibly converted to rate outcomes through offsets. Example analyses are provided for modeling means and dispersions for non-melanoma skin cancer rates for women of varying ages residing in St. Paul, Minnesota and Fort Worth, Texas, addressing how these rates depend on age and location of residence. One of these analyses provides an example for which adaptive modeling distinctly outperforms recommended degree 1 and 2 fractional polynomials.]

Published: Sep 21, 2016

Keywords: Skin Cancer Rates; Xoffset; Voffset; Constant Dispersion Model; Exp Xv

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