Innovative Statistics in Regulatory Science: 1st Edition (Hardback) book cover

Innovative Statistics in Regulatory Science

1st Edition

By Shein-Chung Chow

Chapman and Hall/CRC

456 pages

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Hardback: 9780367224769
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Description

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.

 

 

Table of Contents

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

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.

About the Series

Chapman & Hall/CRC Biostatistics Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General
MED071000
MEDICAL / Pharmacology