张敏， 长聘教授， 万科讲席教授， 博士生导师

专业方向：  生物统计， 健康医疗大数据

教育经历

1997-2001： 北京大学, 环境科学学士，计算机辅修

2001-2004： 杜克大学 (Duke University)，生态学硕士

2004-2008： 北卡州立大学 (North Caroline State University),  统计学博士

工作经历

2008-2015： 密歇根大学 (University of Michigan, Ann Arbor), 生物统计系, 公共卫生学院, 助理教授

2015-2020： 密歇根大学 (University of Michigan, Ann Arbor), 生物统计系, 公共卫生学院, 副教授 (终身)

2020-2023： 密歇根大学 (University of Michigan, Ann Arbor), 生物统计系, 公共卫生学院 教授 (终身)

2023至今： 清华大学，万科公共卫生与健康学院，长聘教授

其它学术任职

（生物）统计期刊： Biometrics， Associate editor

（生物）统计期刊： The International Journal of Biostatistics, editorial board

医学期刊： Journal of Heart and Lung Transplantation, 副主编（Deputy Editor/Statistical Editor），主管统计

泛华统计协会, 董事

研究领域

张敏教授的研究分为两部分: 生物统计方法学和健康医疗领域应用性研究。 在统计方法上研究方向主要有半参数模型和方法，因果推断，最佳个性化治疗规则学习， 临床试验， 生存分析/竞争风险模型， 纵向队列数据分析， 机器（统计）学习，基因和组学分析。此类研究主要是针对新的数据类型和复杂问题提出新的统计模型和计算方法， 成果发表在统计与生物统计期刊上， 如 Journal of the American Statistical Association, Annals of Applied Statistics, Biometrics, Biostatistics, Statistics in Medicine, Lifetime Data Analysis 等。 同时我致力于将统计理论和方法与医疗健康应用紧密结合，与美国多个医院和医学院有着二十年来深入的合作，主持和参与过数十个由美国National Institute of Health, Agency for Healthcare Research and Quality, Centers of Medicare & Medicaid Services 和 National Science Foundation 等资助的大型医疗科研项目。 此类成果发表在医学期刊上， 如Journal of the American Medical Association, Lancet Respiratory Medicine, European Heart Journal, Journal of the American Society of Nephrology, Journal of Heart and Lung Transplantation 等。 应用研究中数据类型主要有临床试验， 医疗保险数据库， 临床注册数据库和大型人群队列研究， 如the Scientific Registry of Transplant Recipients, Interagency Registry for Mechanically Assisted Circulatory Support, the Society of Thoracic Surgeons 等国家级大型医疗数据库和 the Multi-Ethnic Study of Atherosclerosis, Cardiovascular Health Study 等长期纵向人群队列研究。应用领域包括慢性心血管疾病和手术， 器官移植， 肾病， 老年化和免疫， 传染病， 癌症， 盆底功能障碍， 呼吸疾病的多个领域。  在统计和医学期刊发表论文一百二十余篇。

联系方式

本课题组欢迎合作，也招收硕士， 博士，博士后和科研助理。 有意合作或加入课题组请联系mzhangst@tsinghua.edu.cn

学生和科研助理专业要求： 具有统计、生物统计、数学、计算机等相关专业背景同时热爱公共卫生和健康医疗等应用性研究 （不一定需要相关科研经历）， 或者具有公共卫生、流行病学、生物信息学等相关专业背景并且有较强的数理思维和编程能力。

部分论文

Yang, G., Zhang, M., Zhou, S., Hou, H., Grady, K. L., Stewart II, J. W., Chenoweth, C. E., Aaronson, K. D., Fetters, M. D., Chandanabhumma, P. P., Aaronson, K. D., Pienta, M. J.,Malani, P. N., Hider, A. M., Cabrera, L., Pagani, F. D., and Likosky, D. S (2022). Incompleteness of health-related quality of life assessments before left ventricular assist device implant: A novel quality metric. The Journal of Heart and Lung Transplantation, 41(10), 1520-1528.

Yang, G., Zhang, B., Zhang, M. (2022). Estimation of Knots in Linear Spline Model. Journal of the American Statistical Association, Volume 118, 2023 - Issue 541，Pages 639-650.

Fang, Y., Zhang, B., Zhang, M. (2021). Robust Method for Optimal Treatment Decision Making Based on Survival Data. Statistics in Medicine, 40(29):6558-6576.

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, 2022 Mar;10(3):289-297.

Donald S. Likosky, Guangyu Yang, Min Zhang, Preeti N. Malani, Michael D. Fetters, Raymond J. Strobel, Carol E. Chenoweth, Hechuan Hou, Francis D. Pagani. (2022). Interhospital Variability in Healthcare Associated Infections and Payments After Durable Ventricular Assist Device Implant among Medicare Beneficiaries. The Journal of Thoracic and Cardiovascular Surgery, 164(5):1561-1568.

Zhang, B. and Zhang, M. (2021). Subgroup identification and variable selection for treatment decision making. Annals of Applied Statistics, 16(1): 40-59 (March 2022). DOI: 10.1214/21-AOAS1468

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, https://doi.org/10.1111/biom.13492.

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. (2022). A stable and more efficient doubly robust estimator. Statistica Sinica， 32，1143-1163.

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, https://doi.org/10.1002/sim.9168

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, 70(5): 1391–1412.

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.

Shih, T., Zhang, M., Kommareddi, M., Boeve, T.J., Harrington,S.D., Holmes, R.J., Roth, G.,Theurer, P.F., Prager, R.L., Likosky, D.S. (2014). Center-level variation in infection rates after coronary artery bypass grafting. Circulation: Cardiovascular Quality and Outcomes, 7(4),567-573.

Nygaard, I., Brubaker, L., Zyczynski, H.M., Cundiff, G., Richter, H., Gantz, M., Fine, P., Menefee, S., Ridgeway, B., Visco, A., Warren, L.K., Zhang, M., Meikle, S. (2013). Long-term outcomes following abdominal sacrocolpopexy for pelvic organ prolapse. The Journal of the American Medical Association, 309(19), 2016-2024.

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

Valle, J.A., Zhang, M., Dixon, S., Aronow, H.D., Share, D., Naoum, J.B., Gurm, H.S. (2013). Impact of pre-procedural beta blockade on inpatient mortality in patients undergoing primary PCI for ST elevation MI. The American Journal of Cardiology, 111(12), 1714-1720.

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.

Piccini, J. P., Zhang, M., Pieper, K., Solomon, S. D., Al-Khatib, S. M., Van de Werf, F., Pfeffer M. A., McMurray, J.V. J., Califf, R. M., Velazquez, E. J. (2010). Predictors of sudden cardiac death change with time after myocardial infarction: results from the VALIANT Trial. European Heart Journal, 31(2), 211-21.

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).

Wang, T. Y., Zhang, M., Fu, Y., Armstrong, P. W., Newby, L. K., Gibson C. M., Moliterno, D.J., Van de Werf, F., White, H. D., Harrington, R. A., Roe, M. T. (2009). Incidence, distribution,and prognostic impact of occluded culprit arteries among patients with non-ST-elevation acutecoronary syndromes undergoing diagnostic angiography. American Heart Journal, 157(4), 716-723.

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.