Bret Hanlon

Bret Hanlon, PhD is an Associate Professor in the Department of Biostatistics at the University of Michigan School of Public Health, where he serves as Associate Director of Clinical Trials for the SABER Research program. Trained at Cornell University (PhD, 2009), Harvard University (postdoc), and with earlier degrees from Texas Tech University (MS, 2005) and University of North Carolina (BS, 2003), Dr. Hanlon brings extensive expertise in biostatistical methodology and clinical research to his academic role.

Dr. Hanlon's research spans both methodological development and applied statistical collaboration in biomedical and health services research. His methodological work focuses on high-dimensional inference, robust estimation techniques, branching processes with random effects, and regularized ordinal regression. Applied interests center on clinical trial design, particularly cluster- and stepped-wedge randomized trials, and analytic support for independent data monitoring in multi-site industry-sponsored studies. His work spans oncology, cardiology, bariatric surgery, and critical care, with recent projects using machine learning for risk prediction and scenario planning to guide end-of-life decision-making.

Dr. Hanlon's publication record shows consistent productivity, with publications spanning biostatistics methodology, surgical outcomes, cancer research, and obesity studies. His recent work demonstrates increasing focus on machine learning applications in public health and clinical decision support. His methodological contributions in regularized regression and high-dimensional data analysis have been implemented in widely used statistical software packages.

• Aging
• Biostatistics
• Cancer
• Cardiovascular Health
• Clinical Trials
• Health Care
• Health Informatics
• Modeling
• Women's Health

As a biostatistician with deep expertise in clinical trial design and analysis, Dr. Hanlon has served as principal investigator and lead statistician on numerous industry- and federally funded studies, including large-scale trials in oncology, cardiology, and population health. His collaborative work spans academic medicine and independent statistical oversight for pharmaceutical trials, demonstrating his ability to bridge theoretical statistical methodology with practical clinical applications.