Biostatistics Results

How biostatistics can improve cancer care, pediatric heart health and health equity

A University of Michigan researcher explains how data, teamwork and statistical science can reveal disparities, guide care and improve health outcomes

University of Michigan biostatistician Mousumi Banerjee discusses her journey from Kolkata and mathematics to public health, cancer research, pediatric heart care and health equity. In this Q&A, she explains how biostatistics, machine learning and global partnerships can turn complex health data into evidence that improves care and lives for communities.

Aging and the immune system: New research untangles massive datasets to build tools, uncover clues on how we age

A team of University of Michigan experts has received a federal grant to develop advanced statistical and computational methods for analyzing immune data to enhance our understanding of the immune system's role in aging and age-related diseases.

University of Michigan School of Public Health ranks #4 among best schools of public health in the nation

The school also ranked highly in several specialty areas

The University of Michigan School of Public Health has been ranked #4 in the U.S. News & World Report’s list of Best Public Health Schools. The School of Public Health also ranks among the top schools in specialty rankings issued by U.S. News.

U-M School of Public Health professor Peter Song named 2025 AAAS fellow

Biostatistics professor Peter Song has been elected to the American Association for the Advancement of Science's 2025 class, recognized for his contributions to statistical methodology and public health data science.

On the Heights: February 2026

February highlights include faculty expertise on AI-driven cancer research, youth violence prevention, farmworker housing policy, epigenetics and ultra-processed foods, and a new state advisory appointment.

AI tool can predict heart failure from genetic and health record data

A University of Michigan study demonstrates that combining genetic and clinical data for an individual patient can help predict heart failure, the world’s leading cause of hospitalization, a decade before it’s diagnosed.