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Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R: Order-Restricted Analysis of Microarray Data
Paperback
Series: Use R!, Book 0
Medical ReferenceAnatomy & PhysiologyProbability & Statistics
ISBN13: 9783642240065
Publisher: Springer Nature
Published: Aug 26 2012
Pages: 282
Weight: 0.93
Height: 0.60 Width: 6.12 Depth: 9.18
Language: English
This book focuses on the analysis of dose-response microarray data in pharmaceutical settings, the goal being to cover this important topic for early drug development experiments and to provide user-friendly R packages that can be used to analyze this data. It is intended for biostatisticians and bioinformaticians in the pharmaceutical industry, biologists, and biostatistics/bioinformatics graduate students.
Part I of the book is an introduction, in which we discuss the dose-response setting and the problem of estimating normal means under order restrictions. In particular, we discuss the pooled-adjacent-violator (PAV) algorithm and isotonic regression, as well as inference under order restrictions and non-linear parametric models, which are used in the second part of the book.
Part II is the core of the book, in which we focus on the analysis of dose-response microarray data. Methodological topics discussed include:
- Multiplicity adjustment
- Test statistics and procedures for the analysis of dose-response microarray data
- Resampling-based inference and use of the SAM method for small-variance genes in the data
- Identification and classification of dose-response curve shapes
- Clustering of order-restricted (but not necessarily monotone) dose-response profiles
- Gene set analysis to facilitate the interpretation of microarray results
- Hierarchical Bayesian models and Bayesian variable selection
- Non-linear models for dose-response microarray data
- Multiple contrast tests
- Multiple confidence intervals for selected parameters adjusted for the false coverage-statement rate
All methodological issues in the book are illustrated using real-world examples of dose-response microarray datasets from early drug development experiments.
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