Biography
Madhu Mazumdar, Ph.D., M.A., M.S., holds an M.S. in Statistics from the University of Delhi, India, an M.A. in Mathematics from the University of Pittsburgh, and a Ph.D. in Mathematical Statistics from Penn State University. Dr. Mazumdar was recruited by Weill Cornell Medical College (WCMC) in 2004 to become Chief of the then newly created Division of Biostatistics and Epidemiology in the Department of Public Health. She was an Associate Attending Biostatistician at Memorial Sloan-Kettering Cancer Center (MSKCC) before that. Over the past five years she has been highly successful in building the division--crystallizing its mission, creating a viable financial plan, promoting its collaborative and methodological research agenda, and supporting divisional faculty in developing their own research agendas.
Dr. Mazumdar has developed and co-directs courses in introductory and advanced statistical methods for observational studies, including meta-analysis and nutritional epidemiology. She also organizes a seminar series for the division, providing a platform for the faculty and methodologists from the division, neighboring institutions, and beyond to showcase their methodology and collaborative research. She also provides mentorship to the public health, hematology-oncology, nutrition and cancer prevention, and pediatrics fellows and supports their training grants.
Dr. Mazumdar’s research efforts have resulted in over a hundred papers, book chapters, and presentations. She has also successfully obtained numerous collaborative grants. Her collaborations span the subject matters of germ cell tumors, bladder cancer, radiology, surgery; chronic kidney disease, therapeutic medical devices, thoracic surgery, and hematological oncology. Her current grant awards include leading the biostatistics and data management core for the Phase II contract from National Cancer Institute (NCI), co-leading the same core for the Centers for Education and Research on Therapeutics (CERTs) grant from Agency for Healthcare Research and Quality (AHRQ), and leading the Research Design and Biostatistics Core in support of the Clinical Translational Science Award (CTSA) from the National Institutes of Health (NIH). She is a consultant statistical editor of Journal of Experimental Medicine, RU Press, and serves on various data safety monitoring boards.
Her methodological research topics include:
- Public Policy Issues in Cancer Rehabilitation (Cancer Rehabilitation Medicine in Oncology: Editor: Stubblefield, MD; August, 2009)
- Statistical Considerations Underlying Therapeutic Response Criteria for Lung Cancer: A Review in the Context of Emergence of Multi-Slice CT Scanner and Computer Assisted Diagnostic Algorithm for Volumetric Assessment (Optical Society of America Monograph titled “ Developing Imaging Tools for Drug Development: Critical Technology, Clinical Data, and Regulatory Issues; 2008)
- Sequential and Group Sequential Designs In Clinical Trials: Guidelines for Practitioners (Series Title: Epidemiology and Medical Statistics; Publisher: Elsevier; Editors: CR Rao, JP Miller, and DC Rao,2008)
- Design issues underlying strata-matched non-randomized comparative studies with survival outcome (Stat. in Med, 25, 3949-3959, 2006)
- Theoretical approach to choosing the minimum number of multiple tumors required for assessing treatment response. (Journal of Clinical Epidemiology 58(2):150-153, 2005)
- Power and Sample Size Calculation of Comparative Diagnostic Accuracy Studies with Multiple Correlated Test Results Biometrical Journal 47 (2), 140-150, 2005)
- Comparison of therapeutic response criteria (WHO, RECIST) using statistical simulation (J Clin Epidemiol 57(4):358-65, 2004)
- Development of group sequential design for comparative diagnostic accuracy studies (Stat in Med, 22:727-739, 2003; Medical Decision Making, 24(5):525-533, 2004)
- Methodology for finding ‘optimal’ cutpoint for categorizing prognostic variables (Stat in Med 19:113-132; Neuroepidemiology 27(4):188-200, 2006)
- Development of rank test for testing publication bias in meta-analysis (Biometrics 50:1088-1101, 2004)
- Use of cluster analysis for discovering prognostic subgroups (J Clin Oncol, 21(14): 2679-88, 2003)
- Modeling repeated measure of marker values and assessing their prognostic significance (J Clin Oncol 19:2534-2541, 2001)
- Standard therapy outcome adjustment for the effect of patient distributions in cancer clinical trials (Stat in Med 20:883-892, 2001)
- Critique/concern for dosing of Carboplatin in high-dose setting using Calvert’s formulae (J Natl Cancer Inst 92:1434-1436, 2000)
