Innovative Statistics in Regulatory Science

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

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.