5. Poisson Regression

  1. Bee Choo Tai1 and
  2. David Machin2

Published Online: 11 OCT 2013

DOI: 10.1002/9781118721957.ch5

Regression Methods for Medical Research

Regression Methods for Medical Research

How to Cite

Tai, B. C. and Machin, D. (2013) Poisson Regression, in Regression Methods for Medical Research, John Wiley & Sons Ltd, Oxford. doi: 10.1002/9781118721957.ch5

Author Information

  1. 1

    Saw Swee Hock School of Public Health, National University of Singapore and National University Health System; Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore

  2. 2

    Medical Statistics Unit, School of Health and Related Sciences, University of Sheffield; Cancer Studies, Faculty of Medicine, University of Leicester, Leicester, UK

Publication History

  1. Published Online: 11 OCT 2013
  2. Published Print: 29 NOV 2013

ISBN Information

Print ISBN: 9781444331448

Online ISBN: 9781118721957

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Keywords:

  • binomial distribution;
  • cumulative exposure;
  • over-dispersion;
  • Poisson regression analysis;
  • robust estimates;
  • zero-inflated models

Summary

This chapter describes Poisson regression analysis, which may be utilized in situations where the event under study has a very small probability of occurrence and the group under study from which the events occur is large. In this situation the Poisson distribution is a special case of the Binomial distribution and so the regression methods utilized are a special case of logistic regression. Methods of dealing with over-dispersion are described, that is, when the observed variance is larger than would be anticipated from a Poisson distribution, using robust estimates for the estimated regression coefficients and their associated standard errors (SEs). Also included are details of zero-inflated models, which are designed to deal with situations where in some groups there are special reasons why excess counts of zero may be anticipated.