Medical Applications of Finite Mixture Models

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 sum...

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Bibliographic Details
Main Author: Schlattmann, Peter. (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009.
Series:Statistics for Biology and Health,
Subjects:
Online Access:https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-540-68651-4
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505 0 # |a 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. 
520 # # |a 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. 
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