1st Edition
Real-World Evidence in Drug Development and Evaluation
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.
Features
- 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
Introduction
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
Introduction
Statistical methods for population-based cancer registry
Application to small cell lung cancer survival
Discussions
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
Introduction
Bayesian sensitivity analysis for unobserved confounders
Bayesian evidence synthesis using meta-analysis
Discussion
6. Real-World Evidence for Coverage and Payment Decisions
Saurabh Aggarwal*, Hui Huang*, Ozlem Topaloglu, Ross Selby
Introduction
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
Biography
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.
