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Adaptive Regression for Modeling Nonlinear RelationshipsAdaptive Logistic Regression Modeling of Multivariate Dichotomous and Polytomous Outcomes in SAS

Adaptive Regression for Modeling Nonlinear Relationships: Adaptive Logistic Regression Modeling... [This chapter describes how to use the genreg macro for adaptive logistic regression modeling of multivariate dichotomous and polytomous outcomes as described in Chap. 10 as well as its generated output. Example are provided for modeling means and dispersions for post-baseline respiratory status in terms of time, baseline respiratory status, and being on active treatment as opposed to taking a placebo. The analyses consider both dichotomous respiratory status, categorized as poor or good, and polytomous respiratory status, categorized as poor or good or excellent. Ordinal regression and multinomial regression models are considered for polytomous respiratory status. Examples are presented for transition modeling and GEE-based marginal modeling of dichotomous and polytomous respiratory status. An example residual analysis is presented for dichotomized respiratory status.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Adaptive Regression for Modeling Nonlinear RelationshipsAdaptive Logistic Regression Modeling of Multivariate Dichotomous and Polytomous Outcomes in SAS

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References (15)

Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2016
ISBN
978-3-319-33944-3
Pages
239 –251
DOI
10.1007/978-3-319-33946-7_11
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter describes how to use the genreg macro for adaptive logistic regression modeling of multivariate dichotomous and polytomous outcomes as described in Chap. 10 as well as its generated output. Example are provided for modeling means and dispersions for post-baseline respiratory status in terms of time, baseline respiratory status, and being on active treatment as opposed to taking a placebo. The analyses consider both dichotomous respiratory status, categorized as poor or good, and polytomous respiratory status, categorized as poor or good or excellent. Ordinal regression and multinomial regression models are considered for polytomous respiratory status. Examples are presented for transition modeling and GEE-based marginal modeling of dichotomous and polytomous respiratory status. An example residual analysis is presented for dichotomized respiratory status.]

Published: Sep 21, 2016

Keywords: Transition Model; Generalize Estimate Equation; Respiratory Status; Clock Time; Adaptive Model

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