Faculty Profile

Min  Zhang, PhD

Min Zhang, PhD

  • Professor of Biostatistics
  • M4126 SPH II
  • 1415 Washington Heights
  • Ann Arbor, Michigan 48109-2029

Min Zhang is a Professor in the Department of Biostatistics at the University of Michigan (UM). Her statistical methodological research has been focused on semiparametric methods, causal inference (comparative effectiveness analysis), dynamic treatment regimes/statistical learning method for optimal treatment decision making (individualized medicine), survival data analysis/competing risk analysis, missing data and clinical trials. She is interested in using semiparametric methods/theory to develop more robust and efficient methods for analyzing data obtained from clinical trials and observational studies (e.g., clinical registries, health claims data). For example, she develops doubly robust and propensity score-based methods for accounting for confounders robustly in order to make causal inferences from observational studies. Another main research area is to develop statistical learning method for estimating the optimal treatment decision rule, which aims to find the optimal treatment option for each individual patient based on his/her own characteristics as opposed to the treatment with the best average treatment effect. In her methodological and collaborative research, she works on various types of data including, for example, survival outcomes, longitudinal, clustered and hierarchical data.   

Dr. Zhang has been collaborating with researchers at Duke Clinical Research Institute (DCRI), UM Kidney Epidemiology and Cost Center (KECC), the Statistical Analysis of Biomedical and Educational Research Group (SABER) at UM, the UM School of Nursing, the Cardiovascular Center (CVC) and the Department of Cardiac Surgery at UM on various projects in many disease areas (e.g., cancer, end-stage renal disease, solid organ transplantation, cardiovascular diseases). She has served as a co-investigator on many NIH and AHRQ funded research projects. Currently, her collaborative research is mainly on cardiovascular diseases/cardiac surgery, and solid organ transplantation. Her research in cardiovascular disease focuses on understanding the variation in health care practices, quality improvement, and evaluating the effect and impact of clinical practices and patient characteristics in terms of clinical outcomes and health cost. Much of her research relies on national or statewide clinical registries (e.g., the Michigan Society of Thoracic and Cardiovascular Surgeons Quality Collaborative, the Interagency Registry for Mechanically Assisted Circulatory Support) and health claims data (e.g., Medicare claims data and Blue Cross and Blue Shield claims data). She also serves as the statistical editor of the Journal of Heart and Lung Transplantation.

  • Ph.D, Statistics, North Carolina State University, 2008
  • M.A., Ecology, Duke University, 2004
  • B.S., Environmental Science (minor: Computer Science), Peking University, 2001

Yang, G., Zhang, B., Zhang, M. (2021). Estimation of Knots in Linear Spline Model. Journal of the American Statistical Association, in press.

Fang, Y., Zhang, B., Zhang, M. (2021). Robust Method for Optimal Treatment Decision Making Based on Survival Data. Statistics in Medicine, in press.

Dahmer, M.K., Yang, G., Zhang, M., Quasney, M.W., Sapru, A., Weeks, H.M., Sinha, P., Curley, M.A.Q., Delucchi, K.L., Calfee, C.S., Flori, H. (2021). Use of Latent Class Analysis in Identification of Phenotypes in Pediatric Acute Respiratory Distress Syndrome Patients, The Lancet Respiratory Medicine, in press.

Donald S. Likosky, Guangyu Yang, Min Zhang, Preeti N. Malani, Michael D. Fetters, Raymond J. Strobel, Carol E. Chenoweth, Hechuan Hou, Francis D. Pagani. (2021). Interhospital Variability in Healthcare-Associated Infections and Payments After Durable Ventricular Assist Device Implant among Medicare Beneficiaries. The Journal of Thoracic and Cardiovascular Surgery, in press.

Wu, X., Zhang, M., Jin, R., Grunkemeier, G.L., Maynard, C., Hira, R.S., MacKenzie, T.,Hebert, M., He, C., Holmes, S.D., Thompson, M.P., Likosky, D.S. (2021). A Comparison of Statistical Methods for Hospital Performance Assessment. Journal of Hospital Administration, in press.

Zhang, B. and Zhang, M. (2021). Subgroup identification and variable selection for treatment decision making. Annals of Applied Statistics, in press.

Zhang, M., and Zhang, B. (2021). Discussion on “Improving precision and power in randomized trials for COVID-19 treatments using covariate adjustment, for binary, ordinal, and time-to-event outcomes” by David Benkeser, Ivan Diaz, Alex Luedtke, Jodi Segal, Daniel Scharfstein, and Michael Rosenblum. Biometrics, in press.

Youfei Yu, Zhang M., Xu Shi Megan E. V. Caram Roderick J. A. Little Bhramar Mukherjee.(2021). A comparison of parametric propensity score-based methods for causal inference with multiple treatments and a binary outcome. Statistics in Medicine, 40(7):1653-1677.

Zhang, M. and Zhang, B. (2021). A stable and more efficient doubly robust estimator. Statistica

Sinica. In press. doi:10.5705/ss.202019.0265.

Song, Y., Zhou, X., Kang, J., Aung, M. T., Zhang, M., Zhao, W., Needham, B. L., Kardia, S.L.R., Liu, Y., Meeker, J.D., Smith, J.A., and Mukherjee (2021). Bayesian Hierarchical Models for High-Dimensional Mediation Analysis with Coordinated Selection of Correlated Mediators. Statistics in Medicine, in press.

Song, Y., Zhou, X., Kang, J., Aung, M. T., Zhang, M., Zhao, W., Needham, B. L., Kardia, S.L.R., Liu, Y., Meeker, J.D., Smith, J.A., and Mukherjee, B. (2021). Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects. Journal of the Royal Statistical Society C, in press.

Sharma, P., Sui, Z., Zhang, M., Magee, J., Barman, P., Patel, Y., Schluger, A., Walter, K., Biggins, S., Cullaro, G., Wong, R., Lai, J., Jo, J., Sinha, J., VanWagner, L., Verna, E. (2021). Renal outcomes after Simultaneous Liver and Kidney Transplantation (SLKT): Results from the US Multicenter SLKT Consortium. Liver Transplantation, 27(8):1144-1153.

Bourque,J.L., Liang, Q., Pagani, F.D., Zhang, M., Thompson, M.P., Aaronson, K.D., Kormos, R.L., McCullough, J.S., Strobel, R.J., Palmer S., Watt, and T., Likosky, D.S. (2021). Durable Ventricular Assist Device Use in the United States by Geographic Region and Minority Status. The Journal of Thoracic and Cardiovascular Surgery, 161(1):123-33.

Su, F., Prashant Goteti, P., and Zhang, M. (2020). Unleashing the Power of Anomaly Data for Soft Failure Predictive Analytics. Proceedings of IEEE International Test Conference, 2020.

Zhang, M., Wang, S., He, Z., Salvatore, M., and Mukherjee., B. (2019). Interaction analysis under misspecification of main effects: Some common mistakes and simple solutions. Statistics in Medicine, 39(11): 1675-1694.

Song, Y., Zhou, X., Zhang, M., Wei Zhao, W., Yongmei Liu,Y., Kardia, S.L.R., Roux, A.V.D.,Needham, B.L., Smith, J.A., and Bhramar Mukherjee, B. (2019). Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies, Biometrics, 76(3):700-710.

Su, F., Goteti, P., Zhang, M. (2019). On freedom from interference in mixed-criticality systems: a causal learning approach. Proceedings of IEEE International Test Conference, 2019.

Zhang, Z., Liu, C., Ma, S., and Zhang, M. (2019). Estimating Mann-Whitney-type causal effects for right-censored survival outcomes. Journal of Causal Inference, 7(1).

Thompson, M., Pagani, F.D., Liang, Q., Franko, L.R., Zhang, M., McCullough, J.S., Strobel, R.J., Aaronson, K., Kormos, R.L., Likosky, D.S. (2019). Center variation in medicare spending for durable left ventricular assist device implant hospitalization. JAMA Cardiology, Jan 30. (doi:10.1001/jamacardio.2018.4717) (with invited commentary)

Zhang, B. and Zhang, M. (2018). Variable selection for estimating the optimal treatment regimes in the presence of a large number of covariates. Annals of Applied Statistics, 12(4), 2335-2358.

Zhang, B. and Zhang, M. (2018). C-learning: a new classification framework to estimate optimal dynamic treatment regimes. Biometrics, 74(3):891-899.

Liang, Q., Ward, S., Pagani, F.D., Sinha, S.S., Zhang, M., Kormos, R., Aaronson, K.D.,Althouse, A., Kirklin, J.K., Naftel, D., Likosky, D.S. (2018). Linkage of medicare files to the Interagency Registry of Mechanically Assisted Circulatory Support. The Annals of Thoracic Surgery 105(5):1397-1402.

Jung, M.S., Zhang, M., Askren, M.K., Berman, M.G., Peltier, S., Hayes, D.F., Therrien, B., Reuter-Lorenz, P.A., Cimprich, B. (2017). Cognitive dysfunction and symptom burden in womentreated for breast cancer: A prospective behavioral and fMRI analysis. Brain Imaging and Behavior,11(1):86-97. PMID: 26809289.

He, Z., Lee, S., Zhang, M., Smith, J.A., Guo, X., Palmas, W., Kardia, S.L.R., Ionita-Laza1, I., Mukherjee, B. (2017). Rare-variant association tests in longitudinal studies, with an application to the Multi-Ethnic Study of Atherosclerosis (MESA), Genetic Epidemiology, 41(8):801-810.

He, Z., Zhang, M., Lee, S., Smith, J.A., Kardia, S.L.R., Diez Roux, A.V., Mukherjee, B. (2017).Set-based tests for gene-environment interaction in longitudinal studies. Journal of the American Statistical Association, 112(519):966-978.

Strobel, R.J., Liang, Q, Zhang, M., Wu, X., Rogers, M.A.M., Theurer,P.F., Fishstrom, A.B., Harrington, S.D., DeLucia, A., Paone, G., Patel, H.J., Prager, R.L., Likosky, D.S. (2016). A pre-operative risk model for post-operative pneumonia following coronary artery bypass grafting. The Annals of Thoracic Surgery, 102(4):1213-9.

Likosky, D.S., Zhang, M., Paone, G., Collins, J., DeLucia, A., Schreiber, T., Theurer, P., Kazziha, S., Leffler, D., Wunderly, D.J., Gurm, H.S., Prager, R.L. (2016) Impact of Institutional Culture on Rates of Transfusions During Cardiovascular Procedures: The Michigan Experience. American Heart Journal, 174:1-6. (doi: 10.1016/j.ahj.2015.12.019. PMID: 26995363)

He, Z., Zhang, M., Lee, S., Smith, J.A., Guo, X., Palmas, W., Kardia, S.L.R., Roux, A.V.D., Mukherjee, B.(2015). Set-based tests for genetic association in longitudinal studies. Biometrics,71(3):606-15.

Zhang, M. (2015). Robust methods to improve efficiency and reduce bias due to chance imbalance in estimating survival curves in randomized clinical trials. Lifetime Data Analysis, 21(1),119-137.

He, Z., Zhang, M., Zhan, X., and Lu, Q. (2014). Modeling and testing for joint association using a genetic random field model. Biometrics, 70(3),471-479.

Zhang, M. and Wang, Y. (2013). Adjusting for observational secondary treatments in estimating the effects of randomized treatments. Biostatistics, 14(3),491-501.

Zhang, B., Tsiatis, A.A., Davidian, M., Zhang, M., and Laber, E (2012). Estimating optimal treatment regimes from classification perspective. Stat, 1(1), 103-114.

Zhang, M. and Schaubel, D. E. (2012). Double-robust semiparametric estimator for differences in restricted mean lifetimes in observational studies. Biometrics, 68, 999-1009.

Zhang, M. and Schaubel, D. E. (2012). Contrasting treatment-specific survival using double robust estimators. Statistics in Medicine, 31(30), 4255-4268.

Zhang, M. and Wang, Y. (2012). Estimating treatment effects from a randomized trial in the presence of secondary treatment. Biostatistics, 13(4), 625-636.

Zhang, M. and Schaubel, D. E. (2011). Estimating differences in restricted mean lifetime using observational data subject to dependent censoring. Biometrics, 67, 740-749.

Zhang, M., Tsiatis, A. A., Davidian, M., Pieper, K. S., and Mahaffey, K. (2011). Inference on treatment effects from a randomized clinical trial in the presence of premature treatment discontinuation: The SYNERGY trial. Biostatistics, 12(2) 258-269.

Schaubel, D. E. and Zhang, M. (2010). Estimating treatment effects on the marginal recurrent event mean in the presence of a terminating event. Lifetime data analysis, 16(4), 451-477.

Zhang, M. and Gilbert, B. P. (2010). Increasing the efficiency of prevention trials by incorporating baseline covariates. Statistical Applications in Infectious Diseases. Vol. 2: Iss. 1, Article 1. (doi: 10.2202/1948-4690.1002).

Zhang, M., Tsiatis, A.A., and Davidian, M. (2008). Improving efficiency of inferences in randomized clinical trials using auxiliary covariates. Biometrics, 64(3), 707-715.

Tsiatis, A.A., Davidian, M., Zhang, M., and Lu, X. (2008). Covariate adjustment for two-sample treatment comparisons in randomized clinical trials: A principled yet flexible approach. Statisticsin Medicine, 27(23), 4658-4677.

Zhang, M. and Davidian, M. (2008). “Smooth” semiparametric regression analysis for arbitrarily censored time-to-event data. Biometrics, 64(2), 567-576.

  • American Statistical Association (ASA)
  • International Biometric Society (IBS), ENAR
  • International Chinese Statistical Association (ICSA)
  • The International Society for Heart and Lung Transplantation (ISHLT)