Biomarker Analysis in Clinical Trials with R
Biomarker Analysis in Clinical Trials with R
Summary
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.
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Biomarker Analysis in Clinical Trials with R by Nusrat Rabbee
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.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 practiceGoal achieved in such a case.
- Christos T. Nakas, International Society for Clinical Biostatistics, 71, 2021
Nusrat Rabbee is a biostatistician and data scientist at Rabbee & Associates, where she creates innovative solutions to help companies accelerate drug and diagnostic development for patients. Her research interest lies in the intersection of data science and personalized medicine. She has extensive experience in bioinformatics, clinical statistics and high-dimensional data analyses. 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 17 years in the pharmaceutical and diagnostics industry focusing on biomarker development. She has taught statistics at UC Berkeley for 4 years.
| SKU | Unavailable |
| ISBN 13 | 9781032242453 |
| ISBN 10 | 1032242450 |
| Title | Biomarker Analysis in Clinical Trials with R |
| Author | Nusrat Rabbee |
| Series | Chapman And Hall Crc Biostatistics Series |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Taylor & Francis Ltd |
| Year published | 2021-12-13 |
| Number of pages | 228 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |