Sarah J. Ratcliffe, Ph.D., is a Professor of Biostatistics at the University of Virginia. She currently serves as the Senior Vice Chair for Research, and Director of the Division of Biostatistics, in the Department of Public Health Sciences. She joined the UVA faculty in 2018, after 17 years at the University of Pennsylvania.
Dr. Ratcliffe has a background in statistics and computing, with specific training and expertise in the analysis of correlated data, especially longitudinal and functional data, in predictive modeling, as well as expertise in modeling informative missing data / dropout. She is currently Director of the Research Methods core of the UVA CTSA (iTHRIV), MPI of an NIH R01 developing prediction algorithms in transplant patients, and PI of an NIH U24 Data Coordinating Center (DCC) for the DIVA Trial. Previous research projects included being MPI/DCC director for the Sustained Aeration of Infant Lungs (SAIL) trial (U01), and MPI of an NIH R01 developing novel statistical methods for longitudinal biomarker trajectories with informative dropout using functional data analysis techniques.
Dr. Ratcliffe is a co-investigator on various clinical studies, leading to research publications in numerous disease areas, including neonatology, fetal and maternal medicine, women’s health, HIV/AIDS research, cardiology and critical care medicine. She serves as the statistical member on several DSMBs, and is a reviewer for NIH study sections. She currently serves on the Executive Board for the International Biometrics Society (2021-), was the 2019 ENAR President, and was elected a Fellow of the American Statistical Association in 2020.
P.O. Box 800717
Charlottesville, VA 22908-0717
Old Med School, Room 3872
Senior Vice Chair for Research
Professor and Director, Division of Biostatistics
Ph.D., Statistics, Macquarie University, Australia
Longitudinal and functional data analysis, joint modelling / informative dropout, predictive modelling.
Functional data analysis (models for complex curves), joint models for longitudinal and survival data, predictive modeling. Research projects include modeling of changes during labor, and modeling RFM data.
A complete list of published work is available at MyBibliography and Google Scholar.
Code from publications is available on GitHub.