ABSTRACT
This chapter explains the basic concepts behind Poisson regression for count and rate data. Poisson regression is a regression analysis for count and rate data. It is a type of generalized linear models (GLMs) whenever the outcome is count. A reason for using Poisson regression is whenever the number of cases (e.g. deaths and accidents) is small relative to the number of no events (e.g. alive and no accident), then it makes more sense to just get the information from the cases in a population of interest, instead of also getting the information from the non-cases as in typical cohort and case-control studies. For example, in the publicly available COVID-19 data, only the number of deaths were reported along with some basic sociodemographic and clinical information for the cases. Whenever the information for the non-cases are available, it is quite easy to instead use logistic regression for the analysis.
