Modeling Dose Response Microarray Data In Early Drug Development Experiments Using R Order Restricted Analysis Of Microarray Data(1st Edition)
Authors:
Dan Lin ,Ziv Shkedy ,Daniel Yekutieli ,Dhammika Amaratunga ,Luc Bijnens
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Solution Manual Includes
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Book details
ISBN: 3642240062, 978-3642240065
Book publisher: Springer
Book Price $0 : 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 RateAll 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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