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 ChallengesHarry YangIntroductionTraditional Drug Development ParadigmDrug Development ProgressLimitations of Traditional Randomized Controlled TrialsReal World Data and Real World EvidenceReal World DataReal World EvidenceDifferences between RWE and Outcomes of RCTRegulatory PerspectiveProductivity ChallengeFDA Critical Path InitiativeRegulatory Perspectives Pertaining to RWEHistorical Approval Based on RWEAccess to RWDOpportunities of RWE in Drug DevelopmentEarly DiscoveryClinical Study Design and FeasibilityStudy ExecutionMarketing ApplicationProduct LaunchProduct Lifecycle ManagementChallenges with RWE Data Access and QualityTechnological BarriersMethodological ChallengesLack of Data TalentsRegulatory RisksConcluding Remarks 2. Evidence derived from real world data: utility, constraints and cautionsDeepak KhatryWhat is RWD in the context of drug development and clinical practiceWhy 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 survivalDiscussions 4. External Control using RWE and Historical Data in Clinical Development Qing Li, Guang Chen, Jianchang Lin, Andy Chi and Simon DaviesIntroduction 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 UsingExternal Control in Clinical Development 5. Bayesian method for assessing drug safety using real-world evidence Binbing Yu IntroductionBayesian sensitivity analysis for unobserved confoundersBayesian evidence synthesis using meta-analysisDiscussion 6. Real-World Evidence for Coverage and Payment Decisions Saurabh Aggarwal*, Hui Huang*, Ozlem Topaloglu, Ross SelbyIntroductionDefining valueContracting trend/value-based agreeementImportance of RWE for demonstrating valueUse of RWE by payers and health technology assessment agencies 7: Causal Inference for Observational Studies/Real-World Data Bo LuCausal Inference with Real-World DataPropensity Score Adjustment for Observational StudiesSensitivity Analysis for Hidden BiasCase 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 developmentA minimalist overview of deep learning methodsIntroduction 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.