The book shows how to model heterogeneity in medical research with covariate adjusted finite mixture models. The areas of application include epidemiology, gene expression data, disease mapping, meta-analysis, neurophysiology and pharmacology. After an informal introduction the book provides and summarizes the mathematical background necessary to understand the algorithms. The emphasis of the book is on a variety of medical applications such as gene expression data, meta-analysis and population pharmacokinetics. These applications are discussed in detail using real data from the medical literature. The book offers an R package which enables the reader to use the methods for his/her needs.

Introduction - Heterogeneity in Medicine

Modeling Count Data
Theory and Algorithms
Disease Mapping and Cluster Investigations
Modeling Heterogeneity in Psychophysiology
Investigating and Analyzing Heterogeneity in Meta-Analysis
Analysis of Gene Expression Data.