Statistical methods that are commonly used in the review and approval process of regulatory submissions are usually referred to as statistics in regulatory science or regulatory statistics. In a broader sense, statistics in regulatory science can be defined as valid statistics that are employed in the review and approval process of regulatory submissions of pharmaceutical products. In addition, statistics in regulatory science are involved with the development of regulatory policy, guidance, and regulatory critical clinical initiatives related research. This book is devoted to the discussion of statistics in regulatory science for pharmaceutical development. It covers practical issues that are commonly encountered in regulatory science of pharmaceutical research and development including topics related to research activities, review of regulatory submissions, recent critical clinical initiatives, and policy/guidance development in regulatory science.
- Devoted entirely to discussing statistics in regulatory science for pharmaceutical development.
- Reviews critical issues (e.g., endpoint/margin selection and complex innovative design such as adaptive trial design) in the pharmaceutical development and regulatory approval process.
- Clarifies controversial statistical issues (e.g., hypothesis testing versus confidence interval approach, missing data/estimands, multiplicity, and Bayesian design and approach) in review/approval of regulatory submissions.
- Proposes innovative thinking regarding study designs and statistical methods (e.g., n-of-1 trial design, adaptive trial design, and probability monitoring procedure for sample size) for rare disease drug development.
- Provides insight regarding current regulatory clinical initiatives (e.g., precision/personalized medicine, biomarker-driven target clinical trials, model informed drug development, big data analytics, and real world data/evidence).
This book provides key statistical concepts, innovative designs, and analysis methods that are useful in regulatory science. Also included are some practical, challenging, and controversial issues that are commonly seen in the review and approval process of regulatory submissions.
About the author
Shein-Chung Chow, Ph.D. is currently a Professor at Duke University School of Medicine, Durham, NC. He was previously the Associate Director at the Office of Biostatistics, Center for Drug Evaluation and Research, United States Food and Drug Administration (FDA). Dr. Chow has also held various positions in the pharmaceutical industry such as Vice President at Millennium, Cambridge, MA, Executive Director at Covance, Princeton, NJ, and Director and Department Head at Bristol-Myers
Squibb, Plainsboro, NJ. He was elected Fellow of the American Statistical Association and an elected member of the ISI (International Statistical Institute). Dr. Chow is Editor-in-Chief of the Journal of Biopharmaceutical Statistics and Biostatistics Book Series, Chapman and Hall/CRC Press, Taylor & Francis, New York. Dr. Chow is the author or co-author of over 300 methodology papers and 30 books.
Preface
1. Introduction
Introduction
Key Statistical Concepts
Complex Innovative Designs
Practical, Challenging, and Controversial Issues
Aim and Scope of the Book
2. Totality-of-the-Evidence
Introduction
Substantial Evidence
Totality-of-the-evidence
Practical and Challenging Issues
Development of Index for Totality-of-the-Evidence
Concluding Remarks
3. Hypotheses Testing Versus Confidence Interval
Introduction
Hypotheses Testing
Confidence Interval Approach
Two One-sided Tests Procedure and Confidence Interval Approach
A Comparison
Sample Size Requirement
Concluding Remarks
Appendix of Chapter 3
4. Endpoint Selection
Introduction
Clinical Strategy for Endpoint Selection
Translations Among Clinical Endpoints
Comparison of Different Clinical Strategies
A Numerical Study
Development of Therapeutic Index Function
Concluding Remarks
5. Non-inferiority Margin
Introduction
Non-inferiority Versus Equivalence
Non-inferiority Hypotheses
Methods for Selection of Non-inferiority Margin
Strategy for Margin Selection
Concluding Remarks
6. Missing Data
Introduction
Missing Data Imputation
Marginal/Conditional Imputation for Contingency
Test for Independence
Recent Development
Concluding Remarks
7. Multiplicity
General Concepts
Regulatory Perspective and Controversial Issues
Statistical Methods for Multiplicity Adjustment
Gate-keeping Procedures
Concluding Remarks
8. Sample Size
Introduction
Traditional Sample Size Calculation
Selection of Study Endpoints
Multiple-Stage Adaptive Designs
Adjustment with Protocol Amendments
Multi-Regional Clinical Trials
Current Issues
Concluding Remarks
9. Reproducible Research
Introduction
The Concept of Reproducibility Probability
The Estimated Power Approach
Alternative Methods for Evaluation of Reproducibility Probability
Applications
Future Perspectives
10. Extrapolation
Introduction
Shift in Target Patient Population
Assessment of Sensitivity Index
Statistical Inference
An Example
Concluding Remarks
Appendix of Chapter 10
11. Consistency Evaluation
Introduction
Issues in Multi-regional Clinical Trials
Statistical Methods
Simulation Study
An Example
Other Considerations/Discussions
Concluding Remarks
12. Drug Products with Multiple Components
Introduction
Fundamental Differences
Basic Considerations
TCM Drug Development
Challenging Issues
Recent Development
Concluding Remarks
13. Adaptive Trial Design
Introduction
What Is Adaptive Design
Regulatory/Statistical Perspectives
Impact, Challenges, and Obstacles
Some Examples
Strategies for Clinical Development
Concluding Remarks
14. Selection Criteria in Adaptive Dose Finding
Introduction
Criteria for Dose Selection
Practical Implementation and Example
Clinical Trial Simulations
Concluding Remarks
15. Generic Drugs and Biosimilars
Introduction
Fundamental Differences
Quantitative Evaluation of Generic Drugs
Quantitative Evaluation of Biosimilars
General Approach for Assessment of Bioequivalence/Biosimilarity
Scientific Factors and Practical Issues
Concluding Remarks
16. Precision and Personalized Medicine
Introduction
The Concept of Precision Medicine
Design and Analysis of Precision Medicine
Alternative Enrichment Designs
Concluding Remarks
17. Big Data Analytics
Introduction
Basic Considerations
Types of Big Data Analytics
Bias of Big Data Analytics
Statistical Methods for Estimation of ¿ and µP - µN
Concluding Remarks
18. Rare Disease Drug Development
Introduction
Basic Considerations
Innovative Trial Designs
Statistical Methods for Data Analysis
Evaluation of Rare Disease Clinical Trials
Some Proposals for Regulatory Consideration
Concluding Remarks
References
Subject Index
Biography
Shein-Chung Chow, Ph.D. is currently a Professor at Duke University School of Medicine, Durham, NC. He was previously the Associate Director at the Office of Biostatistics, Center for Drug Evaluation and Research, United States Food and Drug Administration (FDA). Dr. Chow has also held various positions in the pharmaceutical industry such as Vice President at Millennium, Cambridge, MA, Executive Director at Covance, Princeton, NJ, and Director and Department Head at Bristol-Myers
Squibb, Plainsboro, NJ. He was elected Fellow of the American Statistical Association and an elected member of the ISI (International Statistical Institute). Dr. Chow is Editor-in-Chief of the Journal of Biopharmaceutical Statistics and Biostatistics Book Series, Chapman and Hall/CRC Press, Taylor & Francis, New York. Dr. Chow is the author or co-author of over 300 methodology papers and 30 books.
