SHORT COURSE 1 (Dec 7 -Dec 8)

The organic and evolving nature of real-world data (RWD) and real-world evidence (RWE) responding to the fit-for-purpose requirements for expanding applications of RWE to address payers, patients, physicians’ need along with supporting regulatory decisions is a defining characteristic of this arena. Randomized controlled clinical trials (RCTs) have been the gold standard for the evaluation of efficacy and safety of medical interventions. However, the costs, duration, practicality, and limited generalizability have incentivized many to look for alternative ways to optimize the process and address unique real-world research questions.  In recent years, we have seen an increasing usage of RWD and RWE in clinical development and life-cycle management. The major impetus behind the interest in the use of RWE is the increased efficiency in drug development, resulting in savings of cost and time, ultimately getting drugs to patients sooner. However, even with the encouragement from regulators and available guidance and literature on the use of RWD and RWE in recent years, many challenges remain. In this short course, based on a to be published book in June 2023 by Springer Nature: “Real-World Evidence in Medical Product Development”, will address these challenges by providing an end-to-end guidance including strategic considerations, state-of-the-art statistical methodology reviews, organization and infrastructure considerations, logistic challenges, and practical use cases. The target audience who may be interested in this short course is anyone involved, or with an interest, in the use of RWE in their research for drug development and healthcare decision making. In particular, the audience may include statisticians, clinicians, pharmacometricians, clinical operation specialists, regulators, and decision makers working in academic or contract research organizations, government, and industry.

Outline: 

Day 1 Morning: RWE to Accelerate Medical Product Development and Fit-for-Use RWD Assessment

• Introduction and background on the need for RWE and RWD in clinical development and life-cycle management along with future directions.
• Existing guidance documents and precedents related to RWE by major regulatory agencies across the world and compare similarities and differences in those concepts in guidance documents from different countries.
• key considerations in forming research questions in RW setting.
• Principles in the assessment of assess fit-for-use RWD sources.
• Advanced analytics for key variables ascertainment, including disease status, exposure, or outcomes.

 Day 1 Afternoon: RWD Standard, Linkage, and Estimand in RW Setting

• Introduction of different RWD standards and importance of common data model
• Enhancing RWD capacities via privacy-preserving linkage
• Key considerations in applying estimand framework to RW studies
• Advanced analytics for personalized medicine using RWD 

Day 2 Morning: Causal Inference and Sensitivity Analysis

• Introduction to potential outcomes, directed acyclic graphs, identifiability assumptions, and causal inference.
• Review of commonly used methods for conducting causal inference: g-formula, inverse probability of treatment weighting (IPTW), doubly robust methods, and targeted learning.
• Overview of sensitivity analysis methods for the missing-at-random assumption, the assumptions made for handing intercurrent events, and identifiability assumptions made for causal inference.
• Causal inference roadmap for deriving RWE from the analysis of RWD.

Day 2 Afternoon: Case Example Studies

• Application of causal-inference roadmap to RW studies via examples.
• Six application examples with analysis on regulatory contexts, quality of data sources, statistical methods, and regulatory decisions.
• Conclusion and future directions

Instructor’s Biography

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 (HTA) statistics at AbbVie. She has a PhD in Biostatistics. Weili’s 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 three books on adaptive design, benefit-risk assessment, and RWE, respectively. She is the co-founder and co-chair of the American Statistical Association (ASA) Biopharmaceutical Section (BIOP) Real-world Evidence Scientific Working Group (SWG) from 2018 to 2022. She is also the founder and co-chair of a newly formed ASA BIOP HTA SWG. 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.

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.  Currently, he serves as a co-chair for the phase III of the ASA-BIOP Real-world Evidence Scientific Working Group and a journal associate editor for Statistics in Biopharmaceutical Research.

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.