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Modeling DoseResponse Microarray Data in Early Drug Development Experiments Using R: OrderRestricted Analysis of Microarray Da,Used
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This book focuses on the analysis of doseresponse microarray data in pharmaceutical settings, the goal being to cover this important topic for early drug development experiments and to provide userfriendly 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 doseresponse setting and the problem of estimating normal means under order restrictions. In particular, we discuss the pooledadjacentviolator (PAV) algorithm and isotonic regression, as well as inference under order restrictions and nonlinear 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 doseresponse microarray data. Methodological topics discussed include: Multiplicity adjustment Test statistics and procedures for the analysis of doseresponse microarray data Resamplingbased inference and use of the SAM method for smallvariance genes in the data Identification and classification of doseresponse curve shapes Clustering of orderrestricted (but not necessarily monotone) doseresponse profiles Gene set analysis to facilitate the interpretation of microarray results Hierarchical Bayesian models and Bayesian variable selection Nonlinear models for doseresponse microarray data Multiple contrast tests Multiple confidence intervals for selected parameters adjusted for the false coveragestatement rateAll methodological issues in the book are illustrated using realworld examples of doseresponse microarray datasets from early drug development experiments.
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