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

This book focuses on the analysis of dose-response microarray data in pharmaceutical setting, the goal being to cover this important topic for early drug development and to provide user-friendly R packages that can be used to analyze dose-response microarray 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 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.