Mousumi Banerjee is a Research Professor in the Department of Biostatistics, and Director of Biostatistics at the Center for Healthcare Outcomes and Policy (CHOP). She received her Ph.D. in Statistics from the University of Wisconsin-Madison. Before joining the University of Michigan, she held faculty appointments at the State University of New York in Buffalo, and at Wayne State University in Detroit. Dr. Banerjee also held visiting faculty appointments at the University of Pretoria in South Africa and the University of Calcutta in India. Her current research interests include tree-structured regression and ensemble methods, survival analysis, competing risks, and multilevel models with applications to cancer epidemiology and health services research. Dr. Banerjee is also a member of the UM Comprehensive Cancer Center.
BIOSTAT524: Biostatistics for Clinical Researchers
BIOSTAT 560: Statistical Methods for Epidemiology
BIOSTAT 664: Statistical Methods for Cancer Research
BIOSTAT 653: Applied Statistics III ANOVA and Linear Mixed Models
Ph.D., Statistics, University of Wisconsin, Madison, 1994
M.S., Statistics, Indian Statistical Institute, Calcutta, India, 1988
B.S., Statistics, Indian Statistical Institute, Calcutta, India, 1986
Research Interests & Projects
My methodological research interest is in tree-based regression and ensemble methods for survival data. This is motivated by the need of clinical researchers to define interpretable prognostic classification rules both for understanding the prognostic structure of data and for designing future clinical trials. I also work on statistical models and methods for analyzing competing risks data. In this context, I have been particularly interested in situations where the exact cause of death is only partially known. Finally, I am interested in multilevel data modelling, with a focus on model diagnostics and goodness of fit procedures. My applied research has focused on studies using data from population-based cancer registries to investigate disparities in patterns of cancer care and outcomes.
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Haymart MR, Banerjee M, Stewart AK, Koenig RJ, Birkmeyer JD, Griggs JJ. (2011). Use of Radioactive Iodine for Thyroid Cancer Journal of the American Medical Association, 306, 721-728.
Banerjee M., Ding Y., Noone A.M (2012). Identifying Representative Trees from Ensembles Statistics in Medicine.
Zhao L, Banerjee M. (2012). Bayesian Piecewise Mixture Model for Racial Disparity in Prostate Cancer Progression Computational Statistics and Data Analysis, 56, 362-369.
Filson C.P., Banerjee M., Wolf S. Jr., Ye Z., Wei J.T., Miller D.C. (2011). Surgeon Characteristics and Long-term Trends in the Adoption of Laparoscopic Radical Nephrectomy Journal of Urology, 185, 2072-2077.
Sen A., Banerjee M., Li Y., Noone A.M. (2010). A Bayesian Approach to Competing Risks with Masked Cause of Death Statistics in Medicine, 29, 1681-1695.
Banerjee M, Noone A.M. (2008). Tree-Based Methods for Survival Data. In M. Segal, S. Datta, J. Fine (Ed.) Statistical Advances in Biomedical Sciences: State of the Art and Future Directions (265-285). Wiley: New York..
Miller, D.C., Saigal, C.S., Banerjee, M., Hanley, J., Litwin, M.S. (2008). Diffusion of Surgical Innovation Among Patients with Kidney Cancer. Cancer, 112, 1708-1717.
Banerjee, M., George, J., Yee, C., Hryniuk, W., Schwartz, K. (2007). Disentangling the Effects of Race on Breast Cancer Treatment Cancer, 110, 2169-2177.
Banerjee M., George J., Song E.Y., Roy A., and Hrynuik W. (2004). Tree-Based Model for Breast Cancer Prognostication. Journal of Clinical Oncology, 22, 2567-2575.
Banerjee M. and Frees E.W. (1997). Influence Diagnostics for Linear Longitudinal Models. Journal of the American Statistical Association, 92, 999-1005.
American Statistical Association
International Biometric Society
International Indian Statistical Association
International Statistical Institute