Chapman and Hall/CRC
202 pages | 38 B/W Illus.
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 trials. 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 numerous 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.
• Analysis of pharmacodynamic biomarkers for lending evidence of 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, including 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 medicine. 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. She has taught Statistics at UC Berkeley for four years.
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.
Part I - Pharmacodynamic Biomarkers1. Introduction 2. Toxicology Studies 3. Bioequivalence studies 4. Cross-sectional profile of pharmacodynamics biomarkers 5. Timecourse profile of pharmacodynamics biomarkers 6. Evaluating multiple biomarkers Part 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 by Treatment Interaction 11. Cutoff Determination of Continuous Predictive Biomarker using Group Sequential Methodology (GSM) 12. Adaptive Threshold Design (ATD) 13. Adaptive Seamless Design (ASD) PART III - Surrogate Endpoints 14. Introduction 15. Requirement # 1: Trial Level - Correlation between Hazard Ratios (HR) in Progression Free Survival (PFS) and Overall Survival (OS) across Trials 16. Requirement # 2: Individual level – Assess the correlation between the surrogate and true endpoints after adjusting for treatment (Rindiv2) 17. Examining the Proportion of Treatment Effect in AIDS Clinical Trials 18. Concluding Remarks Part IV - Combining Multiple Biomarkers 19. Introduction 20. Regression based models 21. Tree based models 22. Cluster Analysis 23. Graphical Models Part V - Biomarker Statistical Analysis Plan