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Medical Applications of Finite Mixture ModelsOverview over the Book

Medical Applications of Finite Mixture Models: Overview over the Book Chapter 1 Overview of the Book What can the reader expect from this book? The book intends to introduce the anal- ysis of heterogeneity applied to medical data. A special tool for this kind of anal- ysis are finite mixture models which may or may not be adjusted for covariates. Using examples from the medical literature, the book shows that these model may be useful in many medical applications. Possible applications range from early drug development to the meta-analysis of clinical studies. Others are disease mapping or the analysis of gene expression data, just to mention a few. Thus, another goal of the book is to provide easy-to-use software to make these methods available for the interested reader; therefore, a detailed description of how to use of the R package CAMAN is part of the book. The book also handles some of the theory of finite mixture models. The un- derstanding of this theory requires some knowledge of convex sets and convex optimization. The book attempts to provide the necessary mathematics needed for convex optimization which is difficult to find in the condensed form needed here. The following pages give a general overview of the book. Introduction Chapter 2 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Medical Applications of Finite Mixture ModelsOverview over the Book

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Publisher
Springer Berlin Heidelberg
Copyright
© Springer-Verlag Berlin Heidelberg 2009
ISBN
978-3-540-68650-7
Pages
1 –6
DOI
10.1007/978-3-540-68651-4_1
Publisher site
See Chapter on Publisher Site

Abstract

Chapter 1 Overview of the Book What can the reader expect from this book? The book intends to introduce the anal- ysis of heterogeneity applied to medical data. A special tool for this kind of anal- ysis are finite mixture models which may or may not be adjusted for covariates. Using examples from the medical literature, the book shows that these model may be useful in many medical applications. Possible applications range from early drug development to the meta-analysis of clinical studies. Others are disease mapping or the analysis of gene expression data, just to mention a few. Thus, another goal of the book is to provide easy-to-use software to make these methods available for the interested reader; therefore, a detailed description of how to use of the R package CAMAN is part of the book. The book also handles some of the theory of finite mixture models. The un- derstanding of this theory requires some knowledge of convex sets and convex optimization. The book attempts to provide the necessary mathematics needed for convex optimization which is difficult to find in the condensed form needed here. The following pages give a general overview of the book. Introduction Chapter 2

Published: Dec 8, 2008

Keywords: Mixture Model; Random Effect Model; Expectation Maximization Algorithm; Childhood Leukemia; Gradient Function

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