1st Edition

Biomarker Analysis in Clinical Trials with R

By Nusrat Rabbee Copyright 2020
    228 Pages 38 B/W Illustrations
    by Chapman & Hall

    228 Pages 38 B/W Illustrations
    by Chapman & Hall

    228 Pages 38 B/W Illustrations
    by Chapman & Hall

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    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.


    • 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.

    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


    17. Examining the Proportion of Treatment Effect in AIDS Clinical


    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


    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.