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Status:
Available5.0
35 reviews
ISBN 10: 0429428375
ISBN 13: 9780429428371
Author: 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.
Section I Pharmacodynamic Biomarkers
1. Introduction
2. Toxicology Studies
2.1 Calculating the Number of Animals
2.2 Partial AUC Method
2.2.1 Means and Variances
2.2.2 Contrasts
2.2.3 R Code
2.3 Related R Package ‘PK’
3. Bioequivalence Studies
3.1 Crossover Equivalence Design and Composite Hypotheses
3.1.1 R Code for Fitting Data from Crossover Design
3.2 Relevant R Packages
3.3 R Package ‘BE’
4. Cross-Sectional Profile of Pharmacodynamics Biomarkers
4.1 Visualization and ANOVA
4.1.1 Mean and Standard Errors
4.2 Violin Plots
5. Timecourse Profile of Pharmacodynamics Biomarkers
5.1 Visualization and Linear Mixed-Effects Model with Repeated Measures
5.1.1 R Code for Plotting Percent Change from Baseline
5.2 Time Varying Biomarker and Time-to-Event Outcome
5.2.1 R Code for Modeling Time-Dependent Covariates in Survival Analysis
5.3 Comparing Three Ways of Modeling Longitudinal Biomarkers and a Time-to-Event Outcome
5.3.1 R Package JM
5.3.2 Analysis of the AIDS Data Set
5.3.2.1 R Code
5.3.2.2 Results
5.4 Forest Plots for Linking Changes in Outcome to Changes in a Single PD Marker
5.4.1 R Code
6. Evaluating Multiple Biomarkers
6.1 Forest Plots for Linking Changes in Outcome to Changes in Multiple PD Marker
6.1.1 R Code
6.2 Network Analysis
6.2.1 R Code
6.2.2 Network Correlation Graph
6.3 Visualization Through Heatmaps
References
Section II Predictive Biomarkers
7. Introduction
7.1 Predictive Marker and Drug Co-development Paradigm
7.2 Statistical Model for Predictive Biomarker with Continuous Clinical Endpoint
7.3 Three Basic Designs for Predictive Biomarker Enabled Trial
8. Operational Characteristics of Proof-of-Concept Trials with Biomarker-Positive and -Negative Subgroups
8.1 Recruitment and Study Duration of Marker Subgroup and Overall Population
8.1.1 Operational Characteristics of the BM+ Group
8.1.2 Operational Characteristics of the Overall Population
8.1.3 Plots of the BM+ Group and Overall Population
8.2 Power Curves for Marker Subgroup and Overall Population
8.3 Multiple Testing for BM+ Subgroup and Overall Population
9. A Framework for Testing Biomarker Subgroups in Confirmatory Trials
9.1 Co-primary Testing: The Overall Population and BM+ Group for Efficacy
9.2 Multiple Primary Testing: The Overall Population and BM+ Group for Efficacy
9.2.1 Dunnett Procedure
9.2.2 Dunnett Procedure—Example
9.2.2.1 Power Calculations
10. Cutoff Determination of Continuous Predictive Biomarker for a Biomarker–Treatment Interaction
10.1 Splitting a Continuous Predictive Biomarker
10.2 The Optimal Split Method of Dichotomizing Continuous Biomarker for Predicting Treatment Effect
10.2.1 Survival Endpoint Model
10.2.2 R Code for Cut Point Selection: Bootstrap Method
10.2.3 Cut Point Selection: R Package ‘bhm’ (Bayesian Method)
11. Cutoff Determination of Continuous Predictive Biomarker Using Group Sequential Methodology
11.1 Holmgren’s GSM Method for Biomarker Subgroup Selection
11.1.1 Two-Step Procedure
11.1.2 Strengths and Weaknesses
11.1.3 R Code
11.2 Sample Size Calculations
12. Adaptive Threshold Design
12.1 The ATD Procedure
13. Adaptive Seamless Design (ASD)
13.1 ASD: A Two-Stage Design
13.2 ASD: Controlling Type I Error
13.3 R Package ‘asd’
13.4 Example and R Code
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