Yan Li
Dr. Yan Li is interested in combining her background in computer sciences, genetic and survey methodology with her research experience with National Cancer Institute to develop statistical methods for efficiently designing and analyzing complex samples in a biomedical setting. Particularly, her interests include improving population representativeness of nonprobability samples, health disparity analyses, genetic association using case-control, cohort and cross-sectional studies and surveys with complex designs.
Departments/Units
Areas of Interest
Core FacultyNonprobability Sample Analyses; Health Disparity Analyses; Survival Data Analyses; Genetic Analyses; Causal Inference Using Survey Data
PostDoc, Biostatistics and Survey Statistics, 2009
Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute
PhD, Survey Methodology, 2006
University of Maryland at College Park
MS, Statistics, 2002
University of Nebraska at Lincoln
MS, Animal Genetics and Breeding, 2000
China Agricultural University, China
BS, Computer Science,1997
Beijing Institute of Technology, China
EPIB 650: Biostatistics
EPIB 660: Analysis of Health Survey Data
EPIB 315: Biostatistics for Public Health Practice
SURV 740: Fundamentals of Inference
SURV 745: Practical Tools for sampling and weighting
SURV 701: Analysis of Complex Sample Data
SURV 742: Inference for Complex Surveys
2024 Honorable Mention, Maryland Research Excellence for demonstrably elevating the visibility and
reputation of the University of Maryland Research Enterprise, UMD
2022 Presidents’ Award, Washington Statistical Society, Chapter of the American Statistical Association
2022 Selected Speaker, Named Distinguish Marris Hansen Lecture 2022, Washington Statistical Society,
American Statistical Association
2021 Fellow, American Statistical Association
2016 Poster Award, Survey Research and Methods Section, Joint Statistical Meeting
2009 Travel Award to Eastern North American Region Workshop for Junior Investigators
2007 Division of Cancer Epidemiology and Genetics (DCEG) Fellows Award for Research Excellence
2006 One of the Best Six Submitted Papers for European Association of Methodology Award
2006 Washington Statistical Society Outstanding Graduate Student Award
2006 Travel Award to the Ninth Meeting of New Researchers in Statistics and Probability
2006 Winner of 2006 Joint Statistical Meeting Student Paper Competition
2005 Travel Award to Joint Statistical Meeting
L. Wang, Y. Li, B.I. Graubard, and H.A. Katki (2024). Data-integration with pweudoweights and survey-calibration: application to developing US-representative lung cancer risk models for use in screening. Journal of the Royal Statistical Society Series A: Statistics in Society (preprint).
L. Wang, Y. Li, B.I. Graubard, and H.A. Katki, (2024). Representative pure risk estimation by using data from epidemiologic studies, surveys, and registries: estimating risks for minority subgroups. Journal of the Royal Statistical Society Series A: Statistics in Society, 187(2), 358-373.
Y. Li (2024). Rejoinder: Comments on “Exchangeability Assumption in Propensity-Score Based Adjustment Methods for Population Mean Estimation Using Non-Probability Samples.” Survey Methodology (accepted).
Y. Li (2024). Exchangeability Assumption in Propensity-Score Based Adjustment Methods for Population Mean Estimation Using Non-Probability Samples. Survey Methodology (accepted).
Y. Li, Michael Fay, Sally Hunsberger, B.I. Graubard (2023). Variable inclusion strategies for effective quota sampling and propensity modeling: an application to SARS-Cov-2 infection prevalence estimation. Journal of Survey Statistics and Methodology, 11 (5), 1204-1228.
Y. Li, K. Irimata, Y. He, J. Parker (2022). Variable inclusion strategies through directed acyclic graphs to adjust health surveys subject to selection bias for producing national estimates. Journal of Official Statistics. 38 (3):875-900. doi: 10.2478/jos-2022-0038.
L. Wang, B.I. Graubard, H.A. Katki, Y. Li. (2022). Efficient and Robust Propensity-Score-Based Methods for Population Inference using Epidemiologic Cohorts. International Statistical Review, 90 (1):146-164.
L. Wang, R. Valliant, Y. Li (2021). Adjusted logistic propensity weighting methods for population inference using nonprobability volunteer-based epidemiologic cohorts. Statistics in Medicine, 40(24):5237-5250. doi:10.1002/sim.9122.
C. Kern, Y. Li, L. Wang (2021). Boosted Kernel Weighting – Using Statistical Learning to Improve Inference from Nonprobability Samples, Journal of Survey Statistics and Methodology, 9(5), 1088-1113. 10.1093/jssam/smaa028.
H. Kalish, C. Klumpp-Thomas, S. Hunsberger, et al. (2021). Undiagnosed SARS-CoV-2 seropositivity during the first 6 months of the COVID-19 pandemic in the United States. Science Translational Medicine, 13 (601):eabh3826. doi:10.1126/scitranslmed.abh3826.
Title: Volunteer-Based Epidemiology Data Analysis to Make Population-Based Inferences
Description: Construct pseudo-weights for epidemiology data to represent the underlying population; Measure Regression Association; Estimate Population Mean/Prevelance, etc.
Authors: Yan Li <yli6@umd.edu>, Lingxiao Wang <lingxiao.wang@virginia.edu>