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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