Yun Li is a Research Associate Professor of Biostatistics. She received her Ph.D. in Biostatistics from the University of Michigan in 2008 and joined the faculty that same year. Prior to her doctoral studies, she worked as a biostatistician at Duke Clinical Research Institute for two years, after she completed her Masters in Biostatistics from the University of North Carolina at Chapel Hill. She is currently a member of the University of Michigan Comprehensive Cancer Center, the Arbor Research Collaborative for Health and the Kidney Epidemiology and Cost Center. She has a wide range of methodological interests and has applied biostatistics to a number of scientific areas.
- Ph.D., Biostatistics, University of Michigan, 2008
- M.S., Biostatistics, University of North Carolina at Chapel Hill, 2001
Research Interests & Projects
I am interested in causal inference, missing data issues, Bayesian inference, surrogate and auxiliary data issues, mediation, intermediate outcomes, cancer statistics, mixed models, survival analysis and observational studies. I am currently developing statistical methods to quantify the causal relationship between an intermediate variable and a true endpoint in the presence of a treatment. I am also examining the impact of unmeasured confounders in observational studies. I am particularly interested in developing statistical methods motivated by real world problems in medical research.
I collaborate primarily in health sciences, particularly in the areas of cancer, liver and kidney disease and cardiovascular disease. In cancer research, I had examined how tissue and serum markers are related to tumor recurrence and survival. I am currently investigating the factors that impact the variation of treatment receipts and the degree of individualized care with the Cancer Surveillance & Outcomes Research Team (CanSORT). The team website is http://www.med.umich.edu/cansort/. I have also been extensively collaborating with Arbor Research on the Dialysis Outcomes and Practice Patterns Study (DOPPS) to investigate how the differences in practice patterns correlate with outcome differences among patients with kidney disease. The study website is http://www.dopps.org/. In the area of liver disease, I have studied the mechanisms and causes of liver disease such as biliary atresia and neonatal hepatitis among young children. These collaborations have great potential in helping understand the factors associated with patient outcomes, improving care and lowering mortality and morbidity. Being a biostatistician, I believe in the importance of having good understanding of the science behind the data and the active involvement in every stage of the medical research.
- Li Y, Kurian AW, Bondarenko I, Taylor J, Ward KC, Hamilton AS, Katz SJ, Hofer TP (2016). The influence of 21-gene recurrence score assay on chemotherapy use in a population-based sample of breast cancer patients. Breast Cancer Research and Treatment.
- Lehmann D, Li Y, Saran R, Li Y. (2016) Strengthening instrumental variables through weighting.Statistics in Biosciences.
- Elliot MR, Conlon ASC, Li Y, Kaciroti N, Taylor JMG. (2015) Surrogacy paradox measures in meta-anlaytic settings.Biostatistics, 16(2):400-12
- Li Y, Lee Y, Wolfe RA, Morgenstern H, Zhang J, Port F and Robinson BM. (2015). On a preference-based instrumental variable approach in reducing unmeasured confounding-by-indicationStatistics in Medicine, 34(7):1150-68.
- Li Y., Schaubel D.E., He, K. (2013). Matching methods for obtaining survival functions to estimate the effect of a time-dependent treatmentStatistics in Biosciences. 1-22.
- Elliott, M.R., Li, Y., Taylor, J.M.G. (2013). Accommodating missingness when assessing surrogacy via principal stratification.Clinical Trials
- Elliott MR, Conlon A, Li Y. (2013) Discussion of Surrogate measures and consistent surrogates''. Biometrics, 69(3): 565--569.
- Li Y, Taylor JMG, Elliott M, and Sargent D (2011). Causal assessment of surrogacy in a meta analysis of colorectal cancer trials. Biostatistics, 12, 478-92.
- Li Y, Taylor JMG, Little RJA (2011). A shrinkage approach for estimating a treatment effect using intermediate biomarker data in clinical trials. Biometrics, 67, 1434-1441.
- Li, Y., Taylor, J.M.G. (2010). Predicting Treatment Effects Using Biomarker Data in a Meta-Analysis of Clinical Trials. Statistics In Medicine, 29, 1875-1889.
- Elliott, M.R., Raghunathan, N., Li, Y. (2010). Bayesian inference for mediation effects using principal stratification with dichotomous mediators and outcomes. Biostatistics, 11, 353-372.
- Sen A, Banerjee M, Li Y and Noone A (2010). A Bayesian approach to competing risks analysis with masked cause of death. Statistics in Medicine, 29, 1681-1695.
- Li Y, Taylor JMG, and Elliott MR. (2010). A Bayesian approach to surrogacy assessment using principal stratification in clinical trials.Biometrics, 66, 523-531.
- Li Y, Taylor JMG, Ten Haken R, Eisbruch A. (2007). The impact of dose on parotid salivary recovery in head and neck cancer patients treated with radiation therapy. International Journal of Radiation Oncology, Biology and Physics, 67(3), 660-9.