Developed from the authors' graduate-level biostatistics course, Applied Categorical and
Count Data Analysis, Second Edition explains how to perform the statistical analysis of …

Count data with excess zeros relative to a Poisson distribution are common in many
biomedical applications. A popular approach to the analysis of such data is to use a zero …

Count data with excess zeros often occurs in areas such as public health, epidemiology,
psychology, sociology, engineering, and agriculture. Zero-inflated Poisson (ZIP) regression …

Overdispersion is a common problem in count data. It can occur due to extra population-
heterogeneity, omission of key predictors, and outliers. Unless properly handled, this can …

Microbiome omics data including 16S rRNA reveal intriguing dynamic associations between
the human microbiome and various disease states. Drastic changes in microbiota can be …

Abstract Hall (2000) has described zero‐inflated Poisson and binomial regression models
that include random effects to account for excess zeros and additional sources of …

In this chapter we provide a summary of different methods for the detection of disease
clusters. First of all, we give a summary of methods for computing estimates of the relative …

For any generalized linear model, the Pearson goodness of fit statistic is the score test
statistic for testing the current model against the saturated model. The relationship between …

Count data with extra zeros are common in many medical applications. The zero‐inflated
Poisson (ZIP) regression model is useful to analyse such data. For hierarchical or correlated …

This paper reviews zero-inflated count models and applies them to modelling annual trends
in incidences of occupational allergic asthma, dermatitis and rhinitis in France. Based on the …