1st Edition
Interface between Regulation and Statistics in Drug Development
With the critical role of statistics in the design, conduct, analysis and reporting of clinical trials or observational studies intended for regulatory purposes, numerous guidelines have been issued by regulatory authorities around the world focusing on statistical issues related to drug development. However, the available literature on this important topic is sporadic, and often not readily accessible to drug developers or regulatory personnel. This book provides a systematic exposition of the interplay between the two disciplines, including emerging themes pertaining to the acceleration of the development of pharmaceutical medicines to serve patients with unmet needs.
Features:
- Regulatory and statistical interactions throughout the drug development continuum
- The critical role of the statistician in relation to the changing regulatory and healthcare landscapes
- Statistical issues that commonly arise in the course of drug development and regulatory interactions
- Trending topics in drug development, with emphasis on current regulatory thinking and the associated challenges and opportunities
The book is designed to be accessible to readers with an intermediate knowledge of statistics, and can be a useful resource to statisticians, medical researchers, and regulatory personnel in drug development, as well as graduate students in the health sciences. The authors’ decades of experience in the pharmaceutical industry and academia, and extensive regulatory experience, comes through in the many examples throughout the book.
CONTENTS
List of figures xi
List of abbreviations xiii
Authors’ Disclosure xvii
Acknowledgment xix
About the Authors xxi
Preface xxiii
Chapter 1 ◾ Fundamental Principles of Clinical Trials 1
1.1 INTRODUCTION 1
1.2 GENERAL STATISTICAL CONSIDERATIONS 5
1.2.1 Statistical Analysis Plan 5
1.2.2 Trial Design 6
1.2.3 Randomization and Blinding 7
1.2.4 Statistical Methodology 7
1.2.5 Reporting and Interpretation of Study Results 9
1.2.6 Data Quality and Software Validity 9
1.3 EVOLVING ROLES OF THE STATISTICIAN IN DRUG
DEVELOPMENT 9
1.4 POTENTIAL STATISTICAL ISSUES IN REGULATORY
REVIEW 14
1.4.1 Data Quality 14
1.4.2 Endpoint Definition 14
1.4.3 Design and Analysis Issues 15
1.4.4 Evaluation of Safety 16
1.4.5 Analysis Populations and Subgroups 16
1.4.6 Assessing Interpretation and Reliability of Results 17
1.5 CONCLUDING REMARKS 17
Chapter 2 ◾ Selected Statistical Topics of Regulatory
Importance 23
2.1 INTRODUCTION 23
2.2 MULTIPLICITY 24
2.2.1 Multiple Endpoints 24
2.2.2 Multiple Testing Over the Course of the Study 28
2.3 MISSING VALUES AND ESTIMANDS 30
2.3.1 General Considerations 30
2.3.2 Missingness Mechanisms 32
2.3.3 Approaches for Missing Data 34
2.3.4 Sensitivity Analyses 36
2.3.5 Estimands and Other Recent Regulatory
Developments 37
2.3.6 Concluding Remarks 40
2.4 NON- INFERIORITY STUDY 41
2.4.1 Efficacy Objective 41
2.4.2 Non- inferiority Hypothesis/ Non- inferiority
Margin 42
2.4.3. Determination of NIM 43
2.4.4 Example: FDA Guidance Document 43
2.4.5 Implications of Choice of NIM 44
2.4.6 Strength of a Non- inferiority Study 45
2.4.7 Synthesis Method for Non- inferiority 46
2.4.8. Summary Points 47
2.4.9 Non- inferiority Study with a Safety Objective 47
2.4.10 Summary Points 49
2.5 INNOVATIVE TRIAL DESIGNS 50
2.5.1 Adaptive Designs 50
2.5.2 Adaptive Randomization 50
2.5.3 Sample Size Reestimation 51
2.5.4 Sequential Designs 53
2.5.5 Adaptive Designs for Dose and Treatment
Selection 54
2.5.6 Adaptive Enrichment Designs 55
2.5.7 Master Protocols 55
2.5.7.1 Basket Trials 56
2.5.7.2 Umbrella Trials 58
2.5.7.3 Platform Trials 58
2.5.7.4 Regulatory and Operational
Considerations with Novel Trials 59
2.6 BAYESIAN ANALYSIS IN A REGULATORY
FRAMEWORK 60
2.6.1 Introduction 60
2.6.2 Potential Areas of Application 62
2.6.3 Regulatory Considerations 64
2.6.4 Challenges with Bayesian Statistics 66
2.6.5 Concluding Remarks 66
2.7 SURROGATE ENDPOINTS AND BIOMARKERS 66
2.7.1 Introduction 66
2.7.2 Statistical Considerations 68
2.7.3 Regulatory Considerations 71
2.7.4 Concluding Remarks 72
2.8 SUBGROUP ANALYSES 73
2.8.1 Introduction 73
2.8.2 Subgroup Analyses in the Traditional
Confirmatory Clinical- Trial Setting 73
2.8.3 Statistical Approaches 74
2.8.4 Reporting and Interpretation of
Subgroup Results 75
2.8.5 Subgroup Analyses in the Changing Clinical- Trial
and Regulatory Setting 76
2.8.6 Conclusion 77
2.9 BENEFIT– RISK ASSESSMENT 78
2.9.1 Introduction 78
2.9.2 Methodological Considerations in Benefit– Risk
Analysis 79
2.9.3 Regulatory Perspectives 81
2.9.4 Benefit– Risk in Health- Technology Assessment 84
2.9.5 Concluding Remarks 84
Chapter 3 ◾ Statistical Engagement in Regulatory
Interactions 97
3.1 INTRODUCTION 97
3.2 INTERNAL BEHAVIORS 98
3.3 DATA- MONITORING COMMITTEE 99
3.4 REGULATORY MEETINGS AND
ADVISORY COMMITTEE MEETINGS 101
3.5 STATISTICAL ROLE IN PROMOTIONAL
MATERIAL AND MEDICAL COMMUNICATION 106
3.6 CONCLUDING REMARKS 108
Chapter 4 ◾ Emerging Topics 111
4.1 THE USE OF RWE TO SUPPORT LICENSING AND
LABEL ENHANCEMENT 111
4.1.1 Introduction 111
4.1.2 Methodological and Operational Considerations 113
4.1.3 Current Regulatory Landscape 117
4.1.4 Concluding Remarks 119
4.2 PATIENT- REPORTED OUTCOMES IN REGULATORY
SETTINGS 120
4.2.1 Introduction 120
4.2.2 Development and Validation of PRO
Instruments 121
4.2.3 Statistical Considerations 123
4.2.4 Regulatory Considerations 125
4.2.5 Concluding Remarks 128
4.3 ARTIFICIAL INTELLIGENCE AND
MODERN ANALYTICS IN REGULATORY SETTINGS 129
4.3.1 Introduction 129
4.3.2 AI in Drug Development 131
4.3.3 Regulatory Experience with Machine
Learning and Artificial Intelligence 132
4.3.4 Concluding Remarks 133
Index 143
Biography
Demissie Alemayehu, PhD, is Vice President and Head of the Statistical Research and Data Science Center at Pfizer Inc. He is a Fellow of the American Statistical Association, has published widely, and has served on the editorial boards of major journals, including the "Journal of the American Statistical Association" and the "Journal of Nonparametric Statistics." Additionally, he has been on the faculties of both Columbia University and Western Michigan University. He has co-authored a monograph entitled, "Patient-Reported Outcomes: Measurement, Implementation and Interpretation," and co-edited another, "Statistical Topics in Health Economics and Outcome Research" both published by Chapman & Hall/CRC Press.
Birol Emir, PhD, is Senior Director and Statistics Lead of Real-World Evidence (RWE) at Pfizer Inc. In addition, Dr. Emir has served as Adjunct Professor of Statistics and Lecturer at Columbia University in New York and as an External PhD Committee Member, Graduate School of Arts and Sciences, Rutgers, The State University of New Jersey. Recently, his primary focuses have been on big data, predictive modelling and genomic data analysis. He has numerous publications in refereed journals, and he has co-edited "Statistical Topics in Health Economics and Outcome Research" published by Chapman & Hall/CRC Press. He has taught many short courses and has given several invited presentations.
Michael Gaffney, PhD, is Vice President, Statistics at Pfizer, and received his Ph.D. from New York University School of Environmental Medicine with his dissertation in the area of multistage model of cancer induction. Dr. Gaffney has spent his 43-year career in pharmaceutical research concentrating in the areas of design and analysis of clinical trials and regulatory interaction for drug approval and product defense. He has interacted with FDA, EMA, MHRA and regulators in Canada and Japan on over 25 distinct regulatory approvals and product issues in many therapeutic areas. Dr. Gaffney has published 40 peer-reviewed articles and presented at numerous scientific meetings in diverse areas of modelling cancer induction, variance components, harmonic regression, factor analysis, propensity scores, meta-analysis, large safety trials and sample size re-estimation. Dr. Gaffney was recently a member of the Council for International Organizations of Medical Sciences (CIOMS) X committee and was a co-author of, CIOMS X: Evidence Synthesis and Meta-Analysis for Drug Safety.
