PhD, Biostatistics, University of Michigan
Research interests include: biomarker evaluation, event history data analysis with application to cardiovascular disease, cancer, Alzheimer's disease, risk prediction modeling, disease progression modeling, electronic health records
Honors include: Outstanding Faculty Mentor (2022); Department Method Publication Award (2015)
Service includes: president-elect, Association for Clinical and Translational Science (2024–2026; president, 2026–2028; past president, 2028–2030); co-chair, 2024 International Chinese Statistical Association (ICSA) Applied Statistics Symposium; Steering Committee Member of Executive Committee: Design and Data Analytics (DaDA) Professional Interests Area (PIA) of the Alzheimer’s Association International Society to Advance Alzheimer’s Research and Treatment (ISTAART) (2022–present); Department of Biostatistics Graduate Admissions (2014–present; co-chair since 2022), Faculty Search Committee (2017–present), and Biostatistics Resource Allocation Committee (chair, 2020–present)
Member of: International Biometric Society (IBS); East North American Region (ENAR); American Statistical Association (ASA); Association for Clinical and Translational Science (ACTStat); Alzheimer’s Association International Society to Advance Alzheimer’s Research and Treatment (ISTAART); International Chinese Statistical Association (ICSA)
More information: biostat.app.vumc.org/DandanLiu
Dr. Liu serves as the faculty biostatistician for the Department of Emergency Medicine collaboration plan and the Vanderbilt Memory & Alzheimer’s Center (VMAC). Her statistical mentorship of early career investigators has led to several funded K23 grant applications. She has also been involved in multiple funded studies, including R01s, as a co-investigator. At VMAC, she plays a critical role in the dissemination of all research activities. Her expert guidance includes but is not limited to longitudinal data analysis, multivariate logistic regression, meta-analysis, item response theory, propensity score matching, factor analysis, and latent class analysis.