Chapter THIRTEEN. Modeling Binomial and Binary Outcomes
Published Online: 11 AUG 2003
DOI: 10.1002/0471467979.ch13
Copyright © 2003 John Wiley & Sons, Inc.
Book Title
Quantitative Methods in Population Health: Extensions of Ordinary Regression
Additional Information
How to Cite
Palta, M. (2003) Modeling Binomial and Binary Outcomes, in Quantitative Methods in Population Health: Extensions of Ordinary Regression, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/0471467979.ch13
Publication History
- Published Online: 11 AUG 2003
- Published Print: 15 AUG 2003
Book Series:
Book Series Editors:
- Walter A. Shewhart,
- Samuel S. Wilks
ISBN Information
Print ISBN: 9780471455059
Online ISBN: 9780471467977
- Summary
- Chapter
Keywords:
- binomial;
- binary;
- logistic;
- PROC LOGIST;
- PROC GENMOD;
- deviance;
- Pearson;
- Hosmer–Lemeshow test;
- probit;
- complementary log–log
Summary
We present logistic regression in the generalized linear model framework. Review of logistic regression and its likelihood as traditionally presented. Example of applying PROC LOGIST to neonatal mortality of VLBW births across three years. Binary versus general binomial outcome. Deviance function for binary and grouped binomial data. Deviance and Pearson chi-squares for goodness of fit testing for grouped data. Hosmer–Lemeshow test. Examples illustrating useful features of PROC GENMOD including ESTIMATE command. Probit and complementary log–log link functions and their motivations from latent variable and rate viewpoints, respectively.