LEADER 00000cam  2200709Ii 4500 
001    1145123392 
003    OCoLC 
005    20200920045854.5 
006    m     o  d         
007    cr cnu|||unuuu 
008    200318s2020    flua    ob    001 0 eng d 
020    9780429428371|qelectronic book 
020    0429428375|qelectronic book 
020    9780429766794|qelectronic book 
020    0429766793|qelectronic book 
020    9780429766800|qelectronic book 
020    0429766807|qelectronic book 
020    9780429766787|qelectronic book 
020    0429766785|qelectronic book 
020    |z9781138368835 
020    |z1138368830 
035    (OCoLC)1145123392|z(OCoLC)1145573020 
035    Taylor & Francis All eBooks 
035    skip4alma 
037    9780429428371|bTaylor & Francis 
040    TYFRS|beng|erda|epn|cTYFRS|dEBLCP|dTYFRS|dOCLCQ|dOCLCF
       |dYDX|dYDXIT|dOCLCO 
049    txum 
050  4 R853.B54|bR33 2020eb 
072  7 MAT|x029000|2bisacsh 
072  7 MED|x071000|2bisacsh 
072  7 MED|x090000|2bisacsh 
072  7 PS|2bicssc 
100 1  Rabbee, Nusrat,|eauthor. 
245 10 Biomarker analysis in clinical trials with R /|cNusrat 
       Rabbee. 
264  1 Boca Raton :|bCRC Press,|c[2020] 
300    1 online resource (xxiii, 204 pages) :|billustrations. 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
490 1  Chapman & Hall/CRC biostatistics series 
500    "A Chapman & Hall book." 
504    Includes bibliographical references and index. 
505 0  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 (R2 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 
506    Available only to authorized UTEP users. 
520    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. 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. 
545 0  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. 
588    Description based on online resource; title from digital 
       title page (viewed on June 23, 2020). 
650  0 Biochemical markers|xStatistical methods. 
650  0 Medicine|xResearch|xStatistical methods. 
650  0 R (Computer program language) 
650  2 Biomarkers|xanalysis. 
650  2 Models, Statistical. 
776 08 |cOriginal|z1138368830|z9781138368835|w(OCoLC)1127853594 
830  0 Chapman & Hall/CRC biostatistics series. 
856 40 |uhttp://0-www.taylorfrancis.com.lib.utep.edu/books/
       9780429428371|zTo access this resource 
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