Bayesian Analysis with R for Drug Development (inbunden)
Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
310
Utgivningsdatum
2019-07-03
Förlag
CRC Press
Illustrationer
36 Tables, black and white
Antal komponenter
1
ISBN
9781138295872
Bayesian Analysis with R for Drug Development (inbunden)

Bayesian Analysis with R for Drug Development

Concepts, Algorithms, and Case Studies

Inbunden Engelska, 2019-07-03
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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.
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Övrig information

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.

Innehållsförteckning

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