1st Edition

Real-World Evidence in Drug Development and Evaluation

Edited By Harry Yang, Binbing Yu Copyright 2021
    190 Pages 30 B/W Illustrations
    by Chapman & Hall

    190 Pages 30 B/W Illustrations
    by Chapman & Hall

    190 Pages 30 B/W Illustrations
    by Chapman & Hall

    Real-world evidence (RWE) has been at the forefront of pharmaceutical innovations. It plays an important role in transforming drug development from a process aimed at meeting regulatory expectations to an operating model that leverages data from disparate sources to aid business, regulatory, and healthcare decision making. Despite its many benefits, there is no single book systematically covering the latest development in the field.

    Written specifically for pharmaceutical practitioners, Real-World Evidence in Drug Development and Evaluation, presents a wide range of RWE applications throughout the lifecycle of drug product development. With contributions from experienced researchers in the pharmaceutical industry, the book discusses at length RWE opportunities, challenges, and solutions.


    • Provides the first book and a single source of information on RWE in drug development
    • Covers a broad array of topics on outcomes- and value-based RWE assessments
    • Demonstrates proper Bayesian application and causal inference for real-world data (RWD)
    • Presents real-world use cases to illustrate the use of advanced analytics and statistical methods to generate insights
    • Offers a balanced discussion of practical RWE issues at hand and technical solutions suitable for practitioners with limited data science expertise

    1. Using Real-world Evidence to Transform Drug Development: Opportunities and Challenges
    Harry Yang

    Traditional Drug Development Paradigm
    Drug Development Progress
    Limitations of Traditional Randomized Controlled Trials
    Real World Data and Real World Evidence
    Real World Data
    Real World Evidence
    Differences between RWE and Outcomes of RCT
    Regulatory Perspective
    Productivity Challenge
    FDA Critical Path Initiative
    Regulatory Perspectives Pertaining to RWE
    Historical Approval Based on RWE
    Access to RWD
    Opportunities of RWE in Drug Development
    Early Discovery
    Clinical Study Design and Feasibility
    Study Execution
    Marketing Application
    Product Launch
    Product Lifecycle Management
    Challenges with RWE
    Data Access and Quality
    Technological Barriers
    Methodological Challenges
    Lack of Data Talents
    Regulatory Risks
    Concluding Remarks

    2. Evidence derived from real world data: utility, constraints and cautions
    Deepak Khatry
    What is RWD in the context of drug development and clinical practice
    Why is RWD important?
    For what purposes can RWD be useful?
    What study designs and statistical methods will be necessary to ensure high quality RWE?
    Some application examples

    3. Real-world evidence from population-based cancer registry
    Binbing Yu

    Statistical methods for population-based cancer registry
    Application to small cell lung cancer survival

    4. External Control using RWE and Historical Data in Clinical Development
    Qing Li, Guang Chen, Jianchang Lin, Andy Chi and Simon Davies

    Introduction of using RWE and Historical Data in Clinical Development
    Single Arm Trial Using External Control for Initial Indication
    Comparison Across Trials with External Control for Label Expansion
    Important Considerations When Designing Studies and Analyzing Data Using
    External Control in Clinical Development

    5. Bayesian method for assessing drug safety using real-world evidence
    Binbing Yu

    Bayesian sensitivity analysis for unobserved confounders
    Bayesian evidence synthesis using meta-analysis

    6. Real-World Evidence for Coverage and Payment Decisions
    Saurabh Aggarwal*, Hui Huang*, Ozlem Topaloglu, Ross Selby

    Defining value
    Contracting trend/value-based agreeement
    Importance of RWE for demonstrating value
    Use of RWE by payers and health technology assessment agencies

    7: Causal Inference for Observational Studies/Real-World Data
    Bo Lu

    Causal Inference with Real-World Data
    Propensity Score Adjustment for Observational Studies
    Sensitivity Analysis for Hidden Bias
    Case study: Propensity Score Matching Design and Sensitivity Analysis for Trauma Care Evaluation

    8. Introduction to Artificial Intelligence and Deep Learning with a Case Study in Analyzing Electronic Health Records for Drug Development
    Xiaomao Li and Qi Tang

    Introduction to AI and overview of break-throughts of AI in drug development
    A minimalist overview of deep learning methods
    Introduction of the big data in clinical space: electronic health record
    A case study of using deep learning to analyze HER
    Introduction to Python and cloud computing


    Harry Yang, Ph.D., is Vice President and Head of Biometrics at Fate Therapeutics. He has 25 years of experience across all aspects of drug research and development, from early target discovery, through pre-clinical, clinical, and CMC programs to regulatory approval and post-approval lifecycle management. He has published 7 statistical books, 15 book chapters, and over 90 peer-reviewed papers on diverse scientific and statistical subjects. He is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP.

    Binbing Yu, Ph.D., is Associate Director in the Oncology Statistical Innovation group at AstraZeneca. He serves as the statistical expert across the whole spectrum of drug R&D, including drug discovery, clinical trials, operation and manufacturing, clinical pharmacology, oncology medical affairs and post-marketing surveillance. He obtained his PhD in Statistics from the George Washington University. His primary research interests are clinical trial design and analysis, cancer epidemiology, causal inference in observation studies, PKPD modeling and Bayesian analysis.