About this book
- Covers a wide range of applications of finite mixture models in the health sciences
- Offers an R package which enables the reader to use the methods for his/her needs
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.
Written for: Scientists and practitioners working in the area of medical statistics
- Health Sciences
- Mixture Models
Table of contents
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.