Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R : Order Restricted Analysis of Microarray Data, Paperback by Lin, Dan (EDT); Shkedy, Ziv (EDT); Yekutieli, Daniel (EDT); Amaratunga, Dhammika (EDT); Bijnens, Luc (EDT), ISBN 3642240062, ISBN-13 9783642240065, Like New Used, Free shipping in the US
<p>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.</p><p>Part I of th 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 th.</p><p>Part II is the core of th, in which we focus on the analysis of dose-response microarray data. Methodological topics discussed include:</p><p>• Multiplicity adjustment</p><p>• Test statistics and procedures for the analysis of dose-response microarray data</p><p>• Resampling-based inference and use of the SAM method for small-variance genes in the data</p><p>• Identification and classification of dose-response curve shapes</p><p>• Clustering of order-restricted (but not necessarily monotone) dose-response profiles</p><p>• Gene set analysis to facilitate the interpretation of microarray results</p><p>• Hierarchical Bayesian models and Bayesian variable selection</p><p>• Non-linear models for dose-response microarray data</p><p>• Multiple contrast tests</p><p>• Multiple confidence intervals for selected parameters adjusted for the false coverage-statement rate</p><p>All methodological issues in th are illustrated using real-world examples of dose-response microarray datasets from early drug development experiments.</p><p></p>
