/content/dam/www/people-profile-photos/z/yingqi-zhao/yingqi-zhao-d.jpg
Zhao
Yingqi Zhao, PhD
- Professor, Biostatistics Program, Public Health Sciences Division, Fred Hutch
- Member, Translational Data Science Integrated Research Center (TDS IRC), Fred Hutch
Background
With the goal of improving patient outcomes, Dr. Yingqi Zhao’s work focuses on developing novel statistical and machine learning methods for personalized medicine, dynamic treatment regimes, disease screening and surveillance, clinical trial design, and electronic health records. Specific applications of her work include improving cancer immunotherapy trial design and analysis, early detection of cancer, health care delivery for complex Type 2 diabetes patients, and childhood obesity surveillance. Dr. Zhao collaborates with the Fred Hutch–based Statistics and Data Management Center for the SWOG Cancer Research Network and Early Detection Research Network.
Education
PhD, Biostatistics, University of North Carolina Chapel Hill, 2012
BS, Statistics, Wuhan University, 2006
Research Interests
Statistical methods for personalized medicine, dynamic treatment regimes and survival analysis using data from both clinical trials and complex observational studies.
Statistical Areas of Interest:
Statistical methods for precision treatment and precision prevention
Design of clinical trials for both therapeutic and early detection purposes
Methods for analyzing data from real-world studies, such as electronic medical records, administrative data
Public health surveillance
Application Areas:
Early detection of cancer
Cancer therapeutics
Cancer survivorship
Diabetes
Awards & Honors
2020 - UNC Biostatistics Grizzle Alumni Award, Biostatistics, UNC-CH
2011 - Best Paper in Biometrics, International Biometrics Society
Current Projects
Improve the Design and Analysis of Randomized Screening Trial in a New Era of Cancer Early Detection
Statistical Methods for Design and Analysis of Cancer Immunotherapy Trials
Developing methods for advancing the early detection of pancreatic ductal adenocarcinoma leveraging electronic medical records data