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Dr. MinJae Lee joined Department of Population & Data Sciences as an Associate Professor in March, 2020. She is currently an Associate Professor of Biostatistics in Peter O’Donnell Jr. School of Public Health. Dr. Lee also serves as Chair of Population Science Protocol Review and Monitoring Committee (PRMC) at Harold C. Simmons Comprehensive Cancer Center. Prior to joining UTSW, she served as an Associate/Assistant Professor of Biostatistics in Division of Clinical & Translational Sciences at University of Texas McGovern Medical School, UTHealth (2012-2020), and a Research Instructor of Biostatistics at School of Medicine, Magee-Womens Research Institute, University of Pittsburgh (2010-2012). Dr. Lee received her Ph.D. degree in Biostatistics at Graduate School of Public Health, University of Pittsburgh in 2010. 

Dr. Lee's research focus centers around developing and applying innovative statistical methods to solve challenges and issues found in everyday research settings. She has gained extensive experience working at multidisciplinary research units of academic medical centers, developing successful collaborations with research investigators of various specialties. Dr. Lee has actively participated in publications and grant submissions for the various studies that require advanced statistical methodology such as cancer prevention/behavioral intervention trial design/evaluation, acute/chronic disease biomarker analysis, highly correlated maternal/child health data analysis, and multi-site/multi-level/longitudinal data modeling. As a result of her extensive collaborative scientific research activities, Dr. Lee became familiar with varying levels of statistical challenges in analyzing complex data; she identified the areas requiring methodologic development and provided solutions to address clinically relevant questions by developing innovative statistical methods and illustrating their proper applications to real data. Dr. Lee developed multiple statistical approaches based on quantile regression, non-/semi-parametric statistical methods or Bayesian methodology to deal with various types of measurement errors in self-reported data, truncated/censored values due to limit of detections in biomarkers or environmental measurements, missing values in longitudinal/multi-level data under various mechanisms. She has also contributed to the university through teaching and serving on committees across campuses. She is a member of American Statistical Associations and an active reviewer/associate editor of several statistical and clinical journals. 

Research Interest

  • Development of statistical methods for analyzing censored/truncated biomarkers or environmental measurements subject to detection limits
  • Imputation/estimation methods development for handling missing and/or censored values in longitudinal data
  • Measurement errors in self-reported data
  • Methodology for data management/quality assurance
  • Multi-level/longitudinal data analysis methods
  • Semi-/non-parametric statistical methods
  • Statistical analysis and design for cancer prevention/behavioral intervention trials
  • Statistical challenges of epidemiological research in maternal/child health
  • Translational research


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