Bayesian Analysis with R for Drug Development: Concepts, Algorithms, and Case Studies, 1st Edition (Hardback) book cover

Bayesian Analysis with R for Drug Development

Concepts, Algorithms, and Case Studies, 1st Edition

By Harry Yang, Steven Novick

Chapman and Hall/CRC

248 pages

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Hardback: 9781138295872
pub: 2019-06-24
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Description

Drug development is an iterative process. The recent publications of regulatory guidelines further entail a lifecycle approach. Blending data from disparate sources, the Bayesian approach provides a flexible framework for drug development. Despite its advantages, the uptake of Bayesian methodologies is lagging behind in the field of pharmaceutical development.

Written specifically for pharmaceutical practitioners, Bayesian Analysis with R for Drug Development: Concepts, Algorithms, and Case Studies,describes a wide range of Bayesian applications to problems throughout pre-clinical, clinical, and Chemistry, Manufacturing, and Control (CMC) development. Authored by two seasoned statisticians in the pharmaceutical industry, the book provides detailed Bayesian solutions to a broad array of pharmaceutical problems.

Features

  • Provides a single source of information on Bayesian statistics for drug development
  • Covers a wide spectrum of pre-clinical, clinical, and CMC topics
  • Demonstrates proper Bayesian applications using real-life examples
  • Includes easy-to-follow R code with Bayesian Markov Chain Monte Carlo performed in both JAGS and Stan Bayesian software platforms
  • Offers sufficient background for each problem and detailed description of solutions suitable for practitioners with limited Bayesian knowledge

Harry Yang, Ph.D., is Senior Director and Head of Statistical Sciences at AstraZeneca. He has 24 years of experience across all aspects of drug research and development and extensive global regulatory experiences. He has published 6 statistical books, 15 book chapters, and over 90 peer-reviewed papers on diverse scientific and statistical subjects, including 15 joint statistical works with Dr. Novick. He is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP as well as Peking University.

Steven Novick, Ph.D., is Director of Statistical Sciences at AstraZeneca. He has extensively contributed statistical methods to the biopharmaceutical literature. Novick is a skilled Bayesian computer programmer and is frequently invited to speak at conferences, having developed and taught courses in several areas, including drug-combination analysis and Bayesian methods in clinical areas. Novick served on IPAC-RS and has chaired several national statistical conferences.

Table of Contents

SECTION I Background

1. Bayesian Statistics in Pharmaceutical Development

Introduction

Overview of Drug Development

Basic Research

Drug Discovery

Formulation

Laboratory Test Methods

Pre-Clinical Studies

Clinical Development

Translational Research

Chemical Manufacturing and Control

Regulatory Registration

Statistics in Drug Research and Development

Bayesian Statistics

Opportunities of Bayesian Approach

Pre-Clinical Development

CMC Development

Clinical Trials

Challenges of Bayesian Approach

Objection to Bayesian

Regulatory Hurdles

Concluding Remarks

2. Basics of Bayesian Statistics

Introduction

Statistical Inference

Research Questions

Probability Distribution

Frequentist Methods

Bayesian Inference

Selection of Priors

Bayesian Computation

Monte Carlo Simulation

Example

Markov Chain Monte Carlo

Computation Tools

BUGS and JAGS

SAS PROC MCMC

Utility of JAGS

Concluding Remarks

3. Bayesian Estimation of Sample Size and Power

Introduction

Sample Size Determination

Frequentist Methods

Bayesian Considerations

Bayesian Approaches

Power and Sample Size

Interim Analysis

Futility and Sample Size

Case Example

Modelling of Overall Survival

Maximum Likelihood Estimation

Futility Analysis

Concluding Remarks

SECION II Pre-Clinical and Clinical Research

4. Pre-Clinical Efficacy Study

Introduction

Evaluation of Lab-Based Drugs in Combination

Background

Statistical Methods

Antiviral Combination

Evaluation of Fixed Dose Combination

Bayesian Survival Analysis

Limitations of Animal Data

Current Methods

Bayesian Solution

Case Example

Concluding Remarks

5. Bayesian Adaptive Design for Phase I Dose-Finding Studies

Introduction

Algorithm-Based Design

3+3 Design

Alternate Algorithm-Based Designs

Advantages and Disadvantages of Algorithm-Based Designs

Model Based Designs

Continual Reassessment Methods

CRM for Phase I Cancer Trials

Escalation with Overdose Control

Escalation Based on Toxicity Intervals

Concluding Remarks

6. Design and Analysis of Phase II Dose-Ranging Studies

Introduction

Phase II Dose-Ranging Studies

Criticisms of Traditional Methods

Model-Based Approaches

Estimating Predictive Precision and Assurance for New Trial

COPD Study

Estimation Method

Concluding Remarks

7. Bayesian Multi-Stage Designs for Phase II Clinical Trials

Introduction

Phase II Clinical Trials

Multi-Stage Designs

Frequentist Approaches

Bayesian Methods

Bayesian Single-Arm Trials

Continuous Monitoring of Single-Arm Trials

Comparative Phase II Studies

Examples

Oncology Trial

Multi-Stage Bayesian Design

Concluding Remarks

SECTION III Chemistry, Manufacturing, and Control

8. Analytical Methods

Introduction

Method Validation

Background

Study Design for Validation of Accuracy and Precision

Current Statistical Methods

Total Error Approach

Bayesian Solutions

Example

Method Transfer

Background

Model

Linear Response

Case Example

Concluding Remarks

9. Process Development

Introduction

Quality by Design

Critical Quality Attributes

Risk of Oncogenicity

Bayesian Risk Assessment

Modeling Enzyme Cutting Efficiency

Bayesian Solution

Example

Design Space

Definition

Statistical Methods for Design Space

Bayesian Design Space

Example

Process Validation

Risk-Based Lifecycle Approach

Method Based on Process Capability

Method Based on Predictive Performance

Determination of Number of PPQ Batches

Concluding Remarks

10. Stability

Introduction

Stability Study

Shelf-Life Estimation

Current Methods

Bayesian Approaches

Examples

Selection of Stability Design

Bayesian Criterion

Setting Release Limits

Concluding Remarks

11. Process Control

Introduction

Quality Control and Improvement

Control Charts

Types of Control Charts

Shewhart I-MR Chart

EWMA Control Chart

CUSUM Control Chart

J-Chart

Multivariate Control Chart

Bayesian Control Charts

Control Chart for Data with Censoring

Control Chart for Discrete Data

Control Limit for Aberrant Data

Product Quality Control Based on Safety Data from Surveillance

Concluding Remarks

About the Authors

Harry Yang is Senior Director and Head of Statistical Sciences at MedImmune. He has 24 years of experience across all aspects of drug research and development and extensive global regulatory experiences. He has published six statistical books, 15 book chapters, and over 90 peer-reviewed papers on diverse scientific and statistical subjects, including 15 joint statistical works with Dr. Novick. Dr. Yang is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP as well as Peking University.

Steven Novick is Director of Statistical Sciences at MedImmune. He has extensively contributed statistical methods to the biopharmaceutical literature. Dr. Novick is a skilled Bayesian computer programmer and is frequently invited to speak at conferences, having developed and taught courses in several areas, including drug-combination analysis and Bayesian methods in clinical areas. He served on IPAC-RS and has chaired several national statistical conferences.

About the Series

Chapman & Hall/CRC Biostatistics Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General
MED072000
MEDICAL / Pharmacy
MED090000
MEDICAL / Biostatistics
REF000000
REFERENCE / General