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Biomarker Analysis in Clinical Trials with R by Nusrat Rabbee: New
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Item specifics
- Condition
- Book Title
- Biomarker Analysis in Clinical Trials with R
- Publication Date
- 2020-04-01
- ISBN
- 9781138368835
About this product
Product Identifiers
Publisher
CRC Press LLC
ISBN-10
1138368830
ISBN-13
9781138368835
eBay Product ID (ePID)
12038844407
Product Key Features
Number of Pages
204 Pages
Publication Name
Biomarker Analysis in Clinical Trials with R
Language
English
Subject
Biostatistics, Probability & Statistics / General, Pharmacology
Publication Year
2020
Type
Textbook
Subject Area
Mathematics, Medical
Series
Chapman and Hall/Crc Biostatistics Ser.
Format
Hardcover
Dimensions
Item Weight
20.5 Oz
Item Length
9.2 in
Item Width
6.1 in
Additional Product Features
Intended Audience
College Audience
LCCN
2021-425677
Dewey Edition
23
Reviews
I can imagine applied statisticians having a hardcover version on their desks near their computer, in a somewhat overused condition, referring to this every now and then for the implementation of the described methods in practice. Goal achieved in such a case. - Christos T. Nakas, International Society for Clinical Biostatistics, 71, 2021
Illustrated
Yes
Dewey Decimal
610.724
Table Of Content
Section I Pharmacodynamic Biomarkers 1. Introduction 2. Toxicology Studies 3. Bioequivalence Studies 4. Cross-Sectional Profile of Pharmacodynamics Biomarkers 5. Timecourse Profile of Pharmacodynamics Biomarkers 6. Evaluating Multiple Biomarkers Section II Predictive Biomarkers 7. Introduction 8. Operational Characteristics of Proof-of-Concept Trials with Biomarker-Positive and -Negative Subgroups 9. A Framework for Testing Biomarker Subgroups in Confirmatory Trials 10. Cutoff Determination of Continuous Predictive Biomarker for a Biomarker-Treatment Interaction 11. Cutoff Determination of Continuous Predictive Biomarker Using Group Sequential Methodology 12. Adaptive Threshold Design 13. Adaptive Seamless Design (ASD) Section III Surrogate Endpoints 14. Introduction 15. Requirement # 1: Trial Level - Correlation Between Hazard Ratios in Progression-Free Survival and Overall Survival Across Trials 16. Requirement # 2: Individual Level - Assess the Correlation Between the Surrogate and True Endpoints After Adjusting for Treatment ( R 2 indiv ) 17. Examining the Proportion of Treatment Effect in AIDS Clinical Trials 18. Concluding Remarks Section IV Combining Multiple Biomarkers 19. Introduction 20. Regression-Based Models 21. Tree-Based Models 22. Cluster Analysis 23. Graphical Models Section V Biomarker Statistical Analysis Plan
Synopsis
The world is awash in data. This volume of data will continue to increase. In the pharmaceutical industry, much of this data explosion has happened around biomarker data. Great statisticians are needed to derive understanding from these data. This book will guide you as you begin the journey into communicating, understanding and synthesizing biomarker data. -From the Foreword, Jared Christensen, Vice President, Biostatistics Early Clinical Development, Pfizer, Inc. Biomarker Analysis in Clinical Trials with R offers practical guidance to statisticians in the pharmaceutical industry on how to incorporate biomarker data analysis in clinical trial studies. The book discusses the appropriate statistical methods for evaluating pharmacodynamic, predictive and surrogate biomarkers for delivering increased value in the drug development process. The topic of combining multiple biomarkers to predict drug response using machine learning is covered. Featuring copious reproducible code and examples in R, the book helps students, researchers and biostatisticians get started in tackling the hard problems of designing and analyzing trials with biomarkers. Features: Analysis of pharmacodynamic biomarkers for lending evidence target modulation. Design and analysis of trials with a predictive biomarker. Framework for analyzing surrogate biomarkers. Methods for combining multiple biomarkers to predict treatment response. Offers a biomarker statistical analysis plan. R code, data and models are given for each part: including regression models for survival and longitudinal data, as well as statistical learning models, such as graphical models and penalized regression models., Biomarker Analysis in Clinical Trials with R offers practical guidance to statisticians in the pharmaceutical industry on how to incorporate biomarker data analysis in clinical trial studies. The book discusses the appropriate statistical methods for evaluating pharmacodynamic, predictive and surrogate biomarkers for delivering increased value in the drug development process. The topic of combining multiple biomarkers to predict drug response using machine learning is covered. Featuring copious reproducible code and examples in R, the book helps students, researchers and biostatisticians get started in tackling the hard problems of designing and analyzing trials with biomarkers. Features : Analysis of pharmacodynamic biomarkers for lending evidence target modulation. Design and analysis of trials with a predictive biomarker. Framework for analyzing surrogate biomarkers. Methods for combining multiple biomarkers to predict treatment response. Offers a biomarker statistical analysis plan. R code, data and models are given for each part: including regression models for survival and longitudinal data, as well as statistical learning models, such as graphical models and penalized regression models. Nusrat Rabbee is a biostatistician and data scientist at Eisai, Inc., where she leads Statistical Methodology in Neurology. Her research is in the development of statistical methods and computational tools for personalized treatment. She has co-discovered the RLMM algorithm for genotyping Affymetrix SNP chips and co-invented a high-dimensional molecular signature for cancer. She has spent over sixteen years in the pharmaceutical and diagnostics industry. She has taught Statistics at UC Berkeley for four years., The book offers practical guidance on how to incorporate biomarker data analysis in clinical trial studies. The book discusses the appropriate statistical methods for evaluating pharmacodynamic, predictive and surrogate biomarkers for delivering increased value in the drug development process.
LC Classification Number
RM301.27.R33 2020
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Seller Feedback (512,512)
- m***m (2292)- Feedback left by buyer.Past 6 monthsVerified purchaseI’m thrilled with my recent purchase . The website was user-friendly, and the product descriptions were accurate. Customer service was prompt and helpful, answering all my questions. My order arrived quickly, well-packaged, and the product exceeded my expectations in quality. I’m impressed with the attention to detail and the overall experience. I’ll definitely shop here again and highly recommend from this seller to others. Thank you for a fantastic experience!
- a***n (43)- Feedback left by buyer.Past 6 monthsVerified purchaseMistakenly ordered a paperback that I thought was a hardcover, not sellers fault; it was described properly on the listing. Seller still processed a refund the day I went to return the item and let me keep the item anyway. A+++ service. Book arrived quickly in great condition and for a great price. Thank you so much! Amazing seller!
- n***c (94)- Feedback left by buyer.Past 6 monthsVerified purchaseseller was communicative about my shipment, media mail took a while and tracking wasn't updated frequently, but seller communicated to me very quickly on status. the item came new and wrapped as described, though the packaging in it was packed wasn't sturdy and falling apart when it got to me.