title: Understanding poisson regression.
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 These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is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.
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Hayat
authors.forename: Melinda
authors.initials: M
authors.lastname: Higgins
mesh_terms: D006801:Humans; D015233:Models, Statistical; D015399:Nursing Research; D016012:Poisson Distribution; D012044:Regression Analysis