Regression Models for Count Data

Abstract

Outcomes in the form of counts are becoming an increasingly popular metric in a wide variety of fields. For example, studying the number of hospital, emergency room, or in-patient doctor's office visits has been a major focal point for many recent health studies. Many investigators want to know the impact of many different variables on these counts and help describe ways in which interventions or therapies might bring those numbers down. Traditional least squares regression was the primary mechanism for studying this type of data for decades. However, alternative methods were developed some time ago that are far superior for dealing with this type of data. The focus of this paper is to illustrate how count regression models can outperform traditional methods while utilizing the data in a more appropriate manner. Poisson Regression and Negative Binomial Regression are popular techniques when the data are overdispersed and using Zero-Inflated techniques for data with many more zeroes than is expected under traditional count regression models. These examples are applied to studies with real data.