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Jaya Satagopan, PhD

Associate Attending Biostatistician

Office Phone




University of Wisconsin

Current Research Interests

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.


Selected peer-reviewed publications:

  1. Satagopan JM, Zhou Q, Oliveria SA, Dusza SW, Weinstock MA, Berwick M, Halpern AC (2011). Properties of preliminary test estimators and shrinkage estimators for evaluating multiple risk factors – Application to questionnaire data from the SONIC study. Journal of the Royal Statistical Society Series C (Applied Statistics), 60: 619 – 632.

  2. Satagopan JM, Elston RC (2013). Evaluation of removable statistical interaction for binary traits. Statistics in Medicine, 32: 1164-1190

  3. Satagopan JM, Sen A, Zhou Q, Lan Q, Rothman N, Langseth H, Engel LS (2015). Bayes and empirical Bayes methods for reduced rank regression models in matched case-control studies. Biometrics, 72: 584 – 595.

  4. Satagopan JM, Iasonos A (2017). Measuring differential treatment benefit across marker specific subgroups: the choice of outcome scale. Contemporary Clinical Trials, doi: 10.1016/j.cct.2017.02.007.

  5. Devlin SM, Satagopan JM (2017). Statistical interactions from a growth curve perspective. Human Heredity, 82: 21-36.

For a complete list of publications: