Interface between Regulation and Statistics in Drug Development

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.

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