E-Books →Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R

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 the likelihood ratio test and non-linear parametric models, which are used in the second part of the book.
Part II is the core of the book. Methodological topics discussed include:
· Multiplicity adjustment
· Test statistics and testing procedures for the analysis of dose-response microarray data
· Resampling-based inference and use of the SAM method at the presence of 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
· Hierarchical Bayesian models and non-linear models for dose-response microarray data
· Multiple contrast tests
All methodological issues in the book are illustrated using four "real-world" examples of dose-response microarray datasets from early drug development experiments.
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