MinJae Lee, Ph.D.
Associate Professor

Dr. MinJae Lee joined Department of Population & Data Sciences and Simmons Comprehensive Cancer Center as an Associate Professor in March, 2020. Prior to joining UTSW, she worked for eight years as an Assistant/Associate Professor of Biostatistics in Division of Clinical & Translational Sciences at University of Texas McGovern Medical School, UTHealth. Dr. Lee received her Ph.D. degree in Biostatistics from University of Pittsburgh in 2010. 

Dr. Lee has developed successful collaborations with research investigators of various specialties and actively participated in publications and grant submissions for the various studies that involve the application of advanced statistical analysis methods. As a result of her extensive collaborative/scientific research activities including cancer prevention/behavioral intervention trials, acute/chronic disease biomarkers analysis, maternal/child health studies, and multi-site longitudinal studies, Dr. Lee became familiar with varying levels of statistical/epidemiological 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, 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 of several statistical and clinical journals.

• 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 data
• Longitudinal/multi-level data analysis methods
• Measurement errors in self-reported data
• Methodology for data management/quality assurance
• 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

Featured Publications

A latent class based imputation method under Bayesian quantile regression framework using asymmetric Laplace distribution for longitudinal medication usage data with intermittent missing values.

Lee M, Rahbar MH, Gensler LS, Brown M, Weisman M, Reveille JD, J Biopharm Stat 2020 30 1 160-177

Home Visit Intervention Promotes Lifestyle Changes: Results of an RCT in Mexican Americans.

Vidoni ML, Lee M, Mitchell-Bennett L, Reininger BM, Am J Prev Med 2019 11 57 5 611-620

A generalized weighted quantile sum approach for analyzing correlated data in the presence of interactions.

Lee M, Rahbar MH, Samms-Vaughan M, Bressler J, Bach MA, Hessabi M, Grove ML, Shakespeare-Pellington S, Coore Desai C, Reece JA, Loveland KA, Boerwinkle E, Biom J 2019 07 61 4 934-954

A Comparison of Mean-Based and Quantile Regression Methods for Analyzing Self-Report Dietary Intake Data

Vidoni ML, Reininger BM, Lee M Journal of Probability and Statistics 2019 9750538 2019 1-5

Harmonization, data management, and statistical issues related to prospective multicenter studies in Ankylosing spondylitis (AS): Experience from the Prospective Study Of Ankylosing Spondylitis (PSOAS) cohort.

Rahbar MH, Lee M, Hessabi M, Tahanan A, Brown MA, Learch TJ, Diekman LA, Weisman MH, Reveille JD Contemp Clin Trials Commun 2018 Sep 11 127-135

A nonparametric method for assessment of interactions in a median regression model for analyzing right censored data.

Lee M, Rahbar MH, Talebi H Stat Methods Med Res 2018 Jan 962280217751518

A multiple imputation method based on weighted quantile regression models for longitudinal censored biomarker data with missing values at early visits.

Lee M, Rahbar MH, Brown M, Gensler L, Weisman M, Diekman L, Reveille JD BMC Med Res Methodol 2018 Jan 18 1 8

Opioid Analgesic Use in Patients with Ankylosing Spondylitis: An Analysis of the Prospective Study of Outcomes in an Ankylosing Spondylitis Cohort.

Dau JD, Lee M, Ward MM, Gensler LS, Brown MA, Learch TJ, Diekman LA, Tahanan A, Rahbar MH, Weisman MH, Reveille JD J. Rheumatol. 2017 Dec

Influence of the neural microenvironment on prostate cancer.

Coarfa C, Florentin D, Putluri N, Ding Y, Au J, He D, Ragheb A, Frolov A, Michailidis G, Lee M, Kadmon D, Miles B, Smith C, Ittmann M, Rowley D, Sreekumar A, Creighton CJ, Ayala G Prostate 2017 Nov

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies.

Vatcheva KP, Lee M, McCormick JB, Rahbar MH, Epidemiology (Sunnyvale) 2016 Apr 6 2