Understanding poisson regression

Matthew J. Hayat, Melinda Higgins

Research output: Contribution to journalArticle

Abstract

Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes.

Original languageEnglish (US)
Pages (from-to)207-215
Number of pages9
JournalThe Journal of nursing education
Volume53
Issue number4
DOIs
StatePublished - 2014
Externally publishedYes

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Poisson Distribution
regression
Comorbidity
Nurses
Research Personnel
comorbidity
statistical method
data analysis
nurse

ASJC Scopus subject areas

  • Nursing(all)
  • Education

Cite this

Understanding poisson regression. / Hayat, Matthew J.; Higgins, Melinda.

In: The Journal of nursing education, Vol. 53, No. 4, 2014, p. 207-215.

Research output: Contribution to journalArticle

Hayat, Matthew J. ; Higgins, Melinda. / Understanding poisson regression. In: The Journal of nursing education. 2014 ; Vol. 53, No. 4. pp. 207-215.
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