Hana Lee, PhD, is a Senior Statistical Reviewer of the Office of Biostatistics in the CDER, FDA. She leads and oversees various FDA-funded projects intended to support development of the agency’s RWE program including multiple Sentinel projects to develop causal inference framework for conducting non-randomized studies, to enhance analytic capacity using machine learning-based methods, and to implement sensitivity analysis for RWE studies at the study design stage, and the BAA project on Targeted Learning. She is currently a co-lead of RWE scientific working group of the American Statistical Association (ASA) Biopharmaceutical Section.

Joo-Yeon Lee is a senior mathematical statistician in Division of Biometrics VII at FDA/CDER. Since she joined FDA in 2007, she developed an expertise on pharmacometrics and design and analysis of post-market drug safety studies. She has reviewed sponsor led studies as well as collaborated with other investigators in FDA-led studies in Sentinel Distributed System and federal data partners. She has played a leading role in causal inference method working group in Division of Biometrics VII. Prior to joining FDA, she earned Ph.D in Biostatistics from Brown University.

Dr. Thomas’ primary interest is causal inference methods, particularly using large data sets such as registries, Medicare claims and electronic health records. She is a co-investigator on the NIH-supported Statistical Methods for Complex Data in Cardiovascular Disease and primary investigator on the AHRQ-supported Matching Methods for Comparative Effectiveness Studies of Longitudinal Treatments. She uses causal inference methods in collaborations in cardiovascular disease (CHAMP-HF, ORBIT-AF registries; ACTION-NCDR and CRUSADE registries linked to Medicare; ARISTOTLE and NAVIGATOR clinical trials) and uterine fibroids (COMPARE-UF). She teaches the causal inference course in the Biostatistics and Bioinformatics Masters and PhD programs.