Biostatistics Research

Our faculty are consistently contributing to the national conversation through their research.

Faculty and students in the Division of Biostatistics are active in many areas of methodological and collaborative research, and regularly publish in high-impact statistics, biostatistics, and bio-medical journals.

Bayesian analysis: Brad Carlin, James Hodges, John Hughes, Cavan Reilly, Eric Lock

Causal inference: Haitao Chu, David Vock, Julian Wolfson

Clinical trials: Brad Carlin, Haitao Chu, John Connett, Lynn Eberly, Birgit Grund, James Hodges, Joseph Koopmeiners, Chap Le, Andrew Mugglin, James Neaton, Kyle Rudser, David Vock, Julian Wolfson

Statistical genetics & computational biology: Saonli Basu, Weihua Guan, Yen Yi Ho, Eric LockWei Pan, Cavan Reilly, Baolin Wu

Screening & diagnostic testing: Haitao Chu, Joseph Koopmeiners, Chap Le

Machine learning: Saonli Basu, Yen Yi Ho, James Hughes, Eric LockWei Pan, Cavan Reilly, David Vock, Susan Wei, Julian Wolfson, Baolin Wu

Medical imaging: Lynn Eberly, John Hughes, Joseph Koopmeiners, Lin Zhang

Meta-analysis: Brad Carlin, Haitao Chu, James Hodges

Spatial statistics: Brad Carlin, James Hodges, John Hughes

Survival analysis: Xianghua Luo, Kyle Rudser, David Vock

We pride ourselves on getting students involved in research early and often. Masters students complete a research-oriented Plan B project in collaboration with a faculty adviser. PhD students are involved in research assistantships starting from their first semester, and typically identify a dissertation adviser during the second year of the program. Dissertations follow the 3-paper model, so our students often graduate with multiple peer-reviewed publications.

Recent Student Project and Dissertation Titles:

  • Cynthia Basu, “Hierarchical Bayesian Models for the Pharmacokinetics & Pharmacodynamics of Lorenzo’s Oil”
  • Jeffrey Boatman, “Estimating the Causal Effect of Solid Organ Transplantation Treatment Regimes on Survival”
  • Kristen Cunanan, “Dose-Finding Using Hierarchical Modeling for Multiple Subgroups”
  • Abhirup Datta, “Environment Pollutants Interpolation Using Dynamic Nearest Neighbor Gaussian Process Model”
  • Caroline Groth, “Bayesian Model for Predicting Exposure in Multiple Exposure Groups”
  • Brandon Lee Koch, “Doubly Robust Estimation of Causal Treatment Effects with the Group Lasso”
  • Patrick Schnell, “A Bayesian Credible Subgroups Approach to Identifying Patient Groups with Positive Treatment Effect”
  • Hong Zhao, “Hierarchical Bayesian Approaches for Detecting Inconsistency in Network Meta-Analysis”
  • Xiaoyue Zhao, “Bayesian Hierarchical Modeling and Inference for Well-Mixed and Two-Zone Models in Industrial Hygiene”

Interdisciplinary research includes collaborations across the University of Minnesota, such as the Medical School, School of Nursing, Veterinary Science, the Carlson School of Management, and the Humphrey Institute for Public Affairs, as well as the Supercomputing Institute and Minnesota Population Center. The volume and scope of our collaborations are such that, on a per-faculty-member basis, the Division of Biostatistics is involved in more sponsored research than any other department or division at the University of Minnesota.

Many faculty in the Division of Biostatistics are members of the following collaborative research units: