This book provides state-of-art statistical methodologies, practical considerations from regulators and sponsors, logistics, and real use cases for practitioners for the uptake of RWE/D. Randomized clinical trials have been the gold standard for the evaluation of efficacy and safety of medical products. However, the cost, duration, practicality, and limited generalizability have incentivized many to look for alternative ways to optimize drug development. This book provides a comprehensive list of topics together to include all aspects with the uptake of RWE/D, including, but not limited to, applications in regulatory and non-regulatory settings, causal inference methodologies, organization and infrastructure considerations, logistic challenges, and practical use cases.

Weili He

Dr. Weili He has over 25 years of experience working in the biopharmaceutical industry. She is currently a Distinguished Research Fellow and head of Medical Affairs and Health Technology Assessment statistics at AbbVie. She has a PhD in Biostatistics. Weilis areas of expertise span across clinical trials, real-world studies and evidence generations, statistical methodologies in clinical trials, observational research, innovative adaptive designs, and benefit-risk assessment.  She is the lead or co-author of more than 60 peer-reviewed publications in statistics or medical journals and lead editor of two books on adaptive design and benefit-risk assessment, respectively. She is the co-founder and co-chair of the American Statistical Association (ASA) Biopharmaceutical Section (BIOP) Real-world Evidence Scientific Working Group from 2018 to 2022. Weili is the BIOP Chair-Elect, Chair, and Past Chair from 2020-2022.  She is also an Associate Editor of Statistics in Biopharmaceutical Research since 2014, and an elected Fellow of ASA since 2018.

Yixin Fang

After he received his PhD in Statistics from Columbia University in 2006, Yixin Fang had been working in academia before he joined AbbVie in 2019. Currently, he is a Research Fellow and Director of Statistics in Medical Affairs and Health Technology Assessment Statistics (MA&HTA Statistics) at AbbVie. Within MA&HTA Statistics, he is Head of the therapeutics areas (TAs) of Eye Care and Specialty and Head of Causal Inference Center (CIC). In this role, he is involved with the design and analysis of Phase IV studies and real-world studies in medical affairs and leading HTA submissions in the TA of Eye Care. In addition, he is active in the statistical community with over 100 peer-reviewed manuscripts and his research interests are in real-world data analysis, machine learning, and causal inference.  

Hongwei Wang

Dr. Hongwei Wang has close to 20 years experience working in the biopharmaceutical industry. He is currently a Research Fellow and Director at Medical Affairs and Health Technology Assessment Statistics of AbbVie. Prior to that, Hongwei worked at Sanofi and Merck with increasing responsibilities. He has been leading evidence planning and evidence generation activities across various therapeutic areas in the fields of real-world studies, network meta-analysis and post-hoc analysis with a mission to support medical affair strategy and optimal reimbursement. Hongwei received his PhD in Statistics from Rutgers University, conducts active methodology research and their applications to different stages of drug development. He serves as coauthor of about 40 manuscripts in peer reviewed journals and over 100 presentations at scientific congresses.

Preface.- Part I. Real-World Data and Evidence to Accelerate Medical Product Development.- The need for real world data/evidence in clinical development and life cycle management, and future directions.- Overview of current RWE/RWD landscape.- Key considerations in forming research questions.- Part II. Fit-for-use RWD Assessment and Data Standards.- Assessment of fit-for-use real-world data sources and applications.- Key variables ascertainment and validation in real-world setting.- Data standards and platform interoperability.- Privacy-preserving data linkage for real-world datasets.- Part III. Causal Inference Framework and Methodologies in RWE Research.- Causal Inference with Targeted Learning for Producing and Evaluating Real-World Evidence.- Framework and Examples of Estimands in Real-World Studies.- Clinical Studies Leveraging Real-World Data Using Propensity Score-Based Methods.- Recent statistical development for comparative effectiveness research beyond propensity-score methods.- Innovative Hybrid Designs and Analytical Approaches leveraging Real-Word Data and Clinical Trial data.- Statistical challenges for causal inference using time-to-event real-world data.- Sensitivity Analyses for Unmeasured Confounding: This is the way.- Sensitivity analysis in the analysis of real-world data.- Personalized medicine with advanced analytics.- Use of Real-World Evidence in Health Technology Assessment Submissions.- Part IV. Application and Case studies.- Examples of applying causal-inference roadmap to real-world studies.- Applications using real-world evidence to accelerate medical product development.- The use of real-world data to support the assessment of the benefit and risk of a medicine to treat spinal muscular atrophy.- Index.