2021 KAPAL–KHIDI Bio & Health Webinar Series는 한미생명과학인 협회 (KAPAL)와 KHIDI 미국지사 웨비나 시리즈입니다.

About this event

KAPAL 7th On-Air Webinar

(2021 KAPAL-KHIDI Webinar Series #2)

Title: Use of Real-World Data and Real-World Evidence in Drug Development

The 21st Century Cures Act established a program to evaluate the potential use of real-world evidence (RWE) to help to support the approval of a new indication for a drug approved and to help to support or satisfy post-approval study requirements. Accordingly, the FDA has been exploring the potential use of Real World Data (RWD) in regulatory decision making. This presentation will start with general overview of Real-World Data and Real-World Evidence in drug development and introduction to observational studies and causal inference. Th challenges with RWD/RWE will be discussed from multiple aspects including data quality, study design and statistical methods to adjust potential confounding. At the end of presentation, case studies will be presented.

About the presenter:

Joo-Yeon Lee, Ph.D.

Senior Statistical Reviewer, FDA

Joo-Yeon Lee, Ph.D. is a senior statistician of Division of Biometrics VII in the Office of Biostatistics in the Center for Drug Evaluation and Research at the FDA. She has received a B.S in statistics from Ewha Womans University, M.A and Ph.D. in Biostatistics from Brown University. Since joining FDA in 2007, Dr. Lee has been working as a pharmacometrics reviewer for new drug applications and a statistical reviewer for post-market drug safety studies. Dr. Lee has been playing a major role in many working groups on causal inference methods within the FDA. In addition, Dr. Lee has involved in FDA-led drug safety studies utilizing sentinel data and other data source such as UK Clinical Practice Research Datalink (CPRD) as a collaborator. Dr. Lee has taught the inverse probability weighting method with a case study at causal inference workshop at FDA in November 2017 and short courses at DIA/FDA Biostatistics Industry and Regulator Forum in April 2018 and ASA Biopharmaceutical section regulatory-industry statistics workshop in September 2020. Currently, Dr. Lee has main interest in the causal inference in observational studies, especially methods for sensitivity analysis for unmeasured confounder(s) and is an active member of quantitative bias analysis working group and serves as FDA statistical collaborator of FDA-funded project that aims to evaluate methods for assessment of potential for bias due to uncontrolled confounding.