Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R : Order Restricted Analysis of Microarray Data (Paperback)

Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R : Order Restricted Analysis of Microarray Data (Paperback)
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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 ...
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    Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R : Order Restricted Analysis of Microarray Data (Paperback)

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    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.

  • Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R : Order Restricted Analysis of Microarray Data (Paperback)

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    Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R: Order Restricted Analysis of Microarray Data

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    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.

  • Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R : Order Restricted Analysis of Microarray Data (Paperback)

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    Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R: Order-Restricted Analysis of Microarray Data (2012 Edition) by Lin, Dan/ Shkedy, Ziv/ Yekutieli, Daniel [Paperback]

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    +Description

    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. *Author: Lin, Dan/ Shkedy, Ziv/ Yekutieli, Daniel *Series Title: Use R *Binding Type: Paperback *Number of Pages: 282 *Publication Date: 2012/08/26 *Language: English *Dimensions: 9.18 x 6.12 x 0.60 inches

 

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    Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R: Order-Restricted Analysis of Microarray Data (2012 Edition) by Lin, Dan/ Shkedy, Ziv/ Yekutieli, Daniel [Paperback]

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Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R : Order Restricted Analysis of Microarray Data (Paperback)

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.