J Nurs Educ. 2014 Apr;53(4):207-15. doi: 10.3928/01484834-20140325-04. Epub 2014 Mar 25.
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 These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is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.
护士调查员通常以计数的形式收集研究数据。传统的数据分析方法在历史上要么将计数数据视为连续且正态分布的，要么将计数分为发生或未发生的类别。这些用于分析计数数据的过时方法已被更合适的统计方法所取代，这些方法利用泊松概率分布来分析计数数据。本文的目的是概述泊松分布及其在泊松回归中的应用。针对标准泊松回归模型的假设违反问题，本文提出了替代方法，包括添加过离散参数或负二项式回归。本文通过 ENSPIRE 研究中的应用实例进行了说明，并为了说明问题，还包括对合并症数据的回归建模。
