Dr. Satagopan’s research program in statistical genetics/genomics focuses on developing and promoting the use of novel statistical techniques for discovering new cancer risk factors using Bayesian penalization techniques, understanding how the benefits of treatment and preventive interventions differ in relation to genomic markers through studies of gene-environment and gene-treatment interactions using parsimonious models, and developing risk stratification approaches to cancer treatment and prevention using dimension reduction techniques. She is the lead statistician of cancer molecular epidemiology studies of nevi, cognitive function in brain tumor patients and animal models of imaging and targeted treatment. She co-ordinates the department’s Quantitative Science Undergraduate Research Experience summer internship program with Drs. Elena Elkin and Kay See Tan, with assistance from Ms. Shireen Lewis. She also teaches the annual eight-week course Statistics for biomedical laboratory researchers for MSKCC’s postdoctoral researchers with Dr. Sujata Patil.
- Order Statistic Formulation of the Power of Two-Stage Genotyping with Fixed Sample Size
- R CODES FOR EMPIRICAL BAYES AND BAYESIAN ANALYSES OF MULTIPLE RISK FACTORS IN MATCHED CASE-CONTROL STUDIES
- Shrinkage estimation for analysis of cognitive functions and other cancer-related outcomes
- Significance Cut-offs for Two-Stage Association Studies
- Statistical interactions and Bayes estimation of log odds in case-control studies. - R Functions
- “Data in Brief” full data set