THIRTEEN. Modeling Binomial and Binary Outcomes

  1. Mari Palta

Published Online: 11 AUG 2003

DOI: 10.1002/0471467979.ch13

Quantitative Methods in Population Health: Extensions of Ordinary Regression

Quantitative Methods in Population Health: Extensions of Ordinary Regression

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

Author Information

  1. Madison, Wisconsin, USA

Publication History

  1. Published Online: 11 AUG 2003
  2. Published Print: 15 AUG 2003

Book Series:

  1. Wiley Series in Probability and Statistics

Book Series Editors:

  1. Walter A. Shewhart and
  2. Samuel S. Wilks

ISBN Information

Print ISBN: 9780471455059

Online ISBN: 9780471467977

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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.