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.

• CONTENTSList of figures xiList of abbreviations xiiiAuthors’ Disclosure xviiAcknowledgment xixAbout the Authors xxiPreface xxiiiChapter 1 ◾ Fundamental Principles of Clinical Trials 11.1 INTRODUCTION 11.2 GENERAL STATISTICAL CONSIDERATIONS 51.2.1 Statistical Analysis Plan 51.2.2 Trial Design 61.2.3 Randomization and Blinding 71.2.4 Statistical Methodology 71.2.5 Reporting and Interpretation of Study Results 91.2.6 Data Quality and Software Validity 91.3 EVOLVING ROLES OF THE STATISTICIAN IN DRUGDEVELOPMENT 91.4 POTENTIAL STATISTICAL ISSUES IN REGULATORYREVIEW 141.4.1 Data Quality 141.4.2 Endpoint Definition 141.4.3 Design and Analysis Issues 151.4.4 Evaluation of Safety 161.4.5 Analysis Populations and Subgroups 161.4.6 Assessing Interpretation and Reliability of Results 171.5 CONCLUDING REMARKS 17Chapter 2 ◾ Selected Statistical Topics of RegulatoryImportance 232.1 INTRODUCTION 232.2 MULTIPLICITY 242.2.1 Multiple Endpoints 242.2.2 Multiple Testing Over the Course of the Study 282.3 MISSING VALUES AND ESTIMANDS 302.3.1 General Considerations 302.3.2 Missingness Mechanisms 322.3.3 Approaches for Missing Data 342.3.4 Sensitivity Analyses 362.3.5 Estimands and Other Recent RegulatoryDevelopments 372.3.6 Concluding Remarks 402.4 NON- INFERIORITY STUDY 412.4.1 Efficacy Objective 412.4.2 Non- inferiority Hypothesis/ Non- inferiorityMargin 422.4.3. Determination of NIM 432.4.4 Example: FDA Guidance Document 432.4.5 Implications of Choice of NIM 442.4.6 Strength of a Non- inferiority Study 452.4.7 Synthesis Method for Non- inferiority 462.4.8. Summary Points 472.4.9 Non- inferiority Study with a Safety Objective 472.4.10 Summary Points 492.5 INNOVATIVE TRIAL DESIGNS 502.5.1 Adaptive Designs 502.5.2 Adaptive Randomization 502.5.3 Sample Size Reestimation 512.5.4 Sequential Designs 532.5.5 Adaptive Designs for Dose and TreatmentSelection 542.5.6 Adaptive Enrichment Designs 552.5.7 Master Protocols 552.5.7.1 Basket Trials 562.5.7.2 Umbrella Trials 582.5.7.3 Platform Trials 582.5.7.4 Regulatory and OperationalConsiderations with Novel Trials 592.6 BAYESIAN ANALYSIS IN A REGULATORYFRAMEWORK 602.6.1 Introduction 602.6.2 Potential Areas of Application 622.6.3 Regulatory Considerations 642.6.4 Challenges with Bayesian Statistics 662.6.5 Concluding Remarks 662.7 SURROGATE ENDPOINTS AND BIOMARKERS 662.7.1 Introduction 662.7.2 Statistical Considerations 682.7.3 Regulatory Considerations 712.7.4 Concluding Remarks 722.8 SUBGROUP ANALYSES 732.8.1 Introduction 732.8.2 Subgroup Analyses in the TraditionalConfirmatory Clinical- Trial Setting 732.8.3 Statistical Approaches 742.8.4 Reporting and Interpretation ofSubgroup Results 752.8.5 Subgroup Analyses in the Changing Clinical- Trialand Regulatory Setting 762.8.6 Conclusion 772.9 BENEFIT– RISK ASSESSMENT 782.9.1 Introduction 782.9.2 Methodological Considerations in Benefit– RiskAnalysis 792.9.3 Regulatory Perspectives 812.9.4 Benefit– Risk in Health- Technology Assessment 842.9.5 Concluding Remarks 84Chapter 3 ◾ Statistical Engagement in RegulatoryInteractions 973.1 INTRODUCTION 973.2 INTERNAL BEHAVIORS 983.3 DATA- MONITORING COMMITTEE 993.4 REGULATORY MEETINGS ANDADVISORY COMMITTEE MEETINGS 1013.5 STATISTICAL ROLE IN PROMOTIONALMATERIAL AND MEDICAL COMMUNICATION 1063.6 CONCLUDING REMARKS 108Chapter 4 ◾ Emerging Topics 1114.1 THE USE OF RWE TO SUPPORT LICENSING ANDLABEL ENHANCEMENT 1114.1.1 Introduction 1114.1.2 Methodological and Operational Considerations 1134.1.3 Current Regulatory Landscape 1174.1.4 Concluding Remarks 1194.2 PATIENT- REPORTED OUTCOMES IN REGULATORYSETTINGS 1204.2.1 Introduction 1204.2.2 Development and Validation of PROInstruments 1214.2.3 Statistical Considerations 1234.2.4 Regulatory Considerations 1254.2.5 Concluding Remarks 1284.3 ARTIFICIAL INTELLIGENCE ANDMODERN ANALYTICS IN REGULATORY SETTINGS 1294.3.1 Introduction 1294.3.2 AI in Drug Development 1314.3.3 Regulatory Experience with MachineLearning and Artificial Intelligence 1324.3.4 Concluding Remarks 133Index 143