Min Zhang, Ph.D., joined the Department of Biostatistics at the University of Michigan (UM) as an Assistant Professor in 2008. Her methodological research has been focused on semiparametric methods, causal inference (comparative effectiveness analysis), survival data analysis, longitudinal data analysis, dynamic treatment regimes (personalized medicine), missing data and clinical trials.
She develops semiparametric methods to improve efficiency and robustness of statistical analyses for data obtained from clinical trials or observational studies (e.g., clinical registries, health claims data). Due to the nature of observational studies, accounting for potential confounding in a robust and efficient manner such that valid inferences can be obtained has always been an important yet challenging problem, especially in the presence of time-dependent confounders. Although confounding may seem less a concern for clinical trials, in reality complications often occur, for example, in cancer clinical trials, rendering properly accounting for confounding and robustness consideration necessary. For example, patients in a clinical trial may fail to take the assigned study treatment, optionally discontinue treatments, start a secondary treatment on his/her own, or violate the study protocol in other ways. Zhang’s research has been focused on developing more robust and efficient methods to handle these issues. She develops methodologies for various type of data including, for example, survival outcomes, longitudinal, clustered or hierarchical data.
Her collaborative research is mainly on the area of cardiovascular disease and she has been collaborating with physicians, surgeons and epidemiologists at the UM Cardiovascular Center (CVC) and the Department of Cardiac Surgery. In addition, since 2007 she continues to collaborate with investigators at Duke Clinical Research Institute on research on cardiovascular disease. Her research in cardiovascular disease focuses on understanding variation in health care practices and quality improvement as well as 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). Another main area of her applied research is in renal disease and solid-organ transplantation. Since 2009, she is a member at the UM Kidney Epidemiology and Cost Center (KECC) and has been working on, for example Scientific Registry of Transplant Recipients (SRTR) project and several projects involving patients with End Stage Renal Disease (ESRD).
- Ph.D, Statistics, North Carolina State University, 2008
- M.A., Ecology, Duke University, 2004
- B.S., Environmental Science (minor: Computer Science), Peking University, 2001
- Zhang, M. (2014). Robust methods to improve efficiency and reduce bias due to chance imbalance in estimating survival curves in randomized clinical trials Lifetime Data Analysis.
- Zhang, M. and Wang, Y. (2013). Adjusting for observational secondary treatments in estimating the effects of randomized treatments. Biostatistics 491-501.
- Zhang, M. and Schaubel, D. E. (2012). Contrasting treatment-specific survival using double-robust estimators. Statistics in Medicine 4255-4268.
- Zhang, M. and Wang, Y (2012). Estimating treatment effects from a randomized trial in the presence of a secondary treatment. Biostatistics 625-636.
- Zhang, M. and Schaubel, D. E. (2012). Double-robust semiparametric estimator for differences in restricted mean lifetimes in observational studies. Biometrics 999-1009.
- Zhang, M. and Schaubel, D. E. (2011). Estimating differences in restricted mean lifetime using observational data subject to dependent censoring. Biometrics 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 258-269.
- Zhang, M. and Gilbert, B. P. (2010). Increasing the efficiency of prevention trials by incorporating baseline covariates. Statistical Applications in Infectious Diseases.
- Zhang, M., Tsiatis, A.A., and Davidian, M. (2008). Improving efficiency of inferences in randomized clinical trials using auxiliary covariates. Biometrics 707-715.
- Zhang, M. and Davidian, M. (2008). "Smooth" semiparametric regression analysis for arbitrarily censored time-to-event data. Biometrics 567-576.
- American Statistical Association
- International Biometric Society, ENAR
- International Society of Clinical Biostatistics (ISCB)