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: Haitao ChuJames Hodges, Joseph Koopmeiners, Thomas Murray, Cavan Reilly, Eric Lock, Lin Zhang

Causal inference: Haitao Chu, Jared Huling, Joseph Koopmeiners, Chap Le, Thomas Murray, Wei Pan, David Vock, Julian Wolfson, Baolin Wu, Tianzhong Yang

Clinical trials: Haitao Chu, John Connett, Anne Eaton, Lynn Eberly, Birgit Grund, Erika Helgeson, James Hodges, Joseph Koopmeiners, Chap Le, Xianghua Luo, Andrew Mugglin, Thomas Murray, James Neaton, Cavan Reilly, Kyle Rudser, David Vock

Statistical genetics & computational biology: Saonli Basu, Weihua Guan, Eric LockWei Pan, Cavan Reilly, Sandra Safo, Baolin Wu, Tianzhong Yang, Lin Zhang

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

Machine learning: Saonli Basu, Lynn Eberly, Erika Helgeson, Jared HulingEric LockWei Pan, Ashley Petersen, Cavan Reilly, Sandra Safo, David Vock, Julian Wolfson

Medical imaging: Lynn Eberly, Mark Fiecas, Joseph Koopmeiners, Baolin Wu, Lin Zhang

Meta-analysis: Haitao Chu, James Hodges, Tianzhong Yang

Spatial statistics: Mark Fiecas, James Hodges, Lin Zhang

Survival analysis: Anne Eaton, Chap Le, Xianghua Luo, Thomas Murray, 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”

Biostatistics working groups are intended for University of Minnesota students and faculty and focus on various areas of faculty research.  This is an opportunity for collaboration, peer-to-peer mentoring, and networking between student cohorts. If you are interested in participating in any of these working groups, please contact the working group leads below.

Working Groups for Fall 2021

Title: Biostats for High-Dimensional Data Working Group

Faculty Lead(s): Ashley Petersen and Sandra Safo

Tentative meeting time: Fridays 1-2 pm, biweekly starting September 24, 2021

Description: The purpose of this working group is to discuss biostatistical methods for analyzing high-dimensional data (i.e., a large number of variables relative to the number of observations). During each meeting, one or two student members present on their own ongoing research or a journal article in this area. We spend the last meeting of the semester discussing a career development book chosen by the group.


Title: Comparative Effectiveness and Research Synthesis (CERS)

Faculty Lead(s): Haitao Chu, Lianne Siegel and James Hodges

Tentative meeting time: Fridays 2-3 pm, biweekly starting September 24, 2021

Description: The mission of our working group is to develop and implement statistical methods to aid in research synthesis and comparative effectiveness research and to foster collaboration among UMN students and faculty conducting research in these areas. Our members develop statistical methods across many areas including multivariate and network meta-analysis, meta-analysis of diagnostic tests, causal inference in meta-analysis, and meta-analysis of normative data from both observational studies and randomized clinical trials. We are also interested in bridging the gap between statistical theory and practice (i.e. translational biostatistics) by publishing peer-reviewed high impact manuscripts that translate recent methodological advances to a clinical and epidemiological audience. We meet regularly to share our current work and discuss journal articles.


Title: Innovative Trial Design Working Group

Faculty Lead(s): Joe Koopmeiners and Tom Murray

Tentative meeting time: Every other Thursday 11 am-12 pm, starting September 23, 2021

Description: The purpose of this working group is to discuss recent advancements and ongoing research in the division related to innovative clinical trial design. Topics to be discussed include Bayesian adaptive methods in clinical trials, master protocols (platform trials, basket trials, etc.), SMARTs/adaptive intervention designs, and other topics related to innovative clinical trial designs.


Title: MERGE (Mobile & E-Health Research Group)

Faculty Lead(s): Julian Wolfson

Tentative meeting time:  Fridays 2:30-3:30 pm (every other week)

Description: The MERGE working group is focused on the applications of statistics to mobile and electronic health data, including data collected from smartphones, wearable sensors, and administrative health databases (EHR). Methods for these data are diverse and developing, but include machine learning, causal inference, and functional data analysis. Group meetings are open to students at any stage of their program, and will include presentation of ongoing projects, discussion of research papers, and tutorials on relevant technologies and skills.


Title: Spatio-temporal Modeling Group

Faculty Lead(s): Mark Fiecas

Tentative meeting time: Fridays 9:30-10:15am, biweekly starting September 17, 2021

Description: This group is largely driven by the students, with a student leading each meeting on a topic of their choice. In previous years, students led discussions of preliminary results for ongoing research projects, professional development, and discussions on a paper that they found interesting. The research topic is often in the context of spatial/temporal modeling – last year’s topics varied from brain imaging to art. This group can be a great way for you to be exposed to and connect with others interested in this area of research.


Title: Statistical Genetics/Omics Journal Club

Faculty Lead(s): Saonli Basu

Tentative meeting time: Fridays 3-4 pm, weekly starting September 17, 2021

Description: The focus of the journal club is to introduce you to methodological developments and applications of genomics/omics in different scientific domains.  Each semester we will choose a topic and have presentations from students and faculty about their research,  discuss the University and outside resources to work in that field. This journal club is supported by the training grant `Interdisciplinary Biostatistics Training in Genetics and Genomics’. See the following webpage for more information:

View Omics Journal Club website


Title: Survival Analysis Working Group

Faculty Lead(s): Anne Eaton and Xianghua Luo

Tentative meeting time: Every other Tuesday 12:30-1:30 pm, starting October 19, 2021

Description: We will meet to discuss biostatistical methods for time-to-event data, including discussing papers, sharing ongoing research and attending and discussing webinars.


Title: Teaching and Communication

Faculty Lead:  Ann Brearley

Tentative Meeting Time: First and third Fridays of every month, 1:30-2:30 pm 

Description: We will focus on a range of topics potentially including teaching methods, anti-racism in teaching, course development, curriculum development, and effective communication with non-statisticians. The focus may shift from semester to semester to follow the interests of the participants. Talks could include reviewing and discussing journal articles, sharing students’ experiences with teaching and outreach, practicing teaching activities, presenting teaching-related research in progress, practicing talks for professional meetings, etc.

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:

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