Ronald Gangnon

Professor of Biostatistics

University of Wisconsin-Madison


I am a Professor in the Department of Biostatistics and Medical Informatics and the Department of Population Health Sciences in the School of Medicine and Public Health at the University of Wisconsin-Madison. I have an affiliate appointment in the Department of Statistics.

I grew up in Duluth, Minnesota. I graduated from East High School in 1988. I received a BA in Mathematics and Economics in 1992 from the University of Minnesota-Duluth and an MS in Statistics in 1994 and a PhD in Statistics with emphasis in Biostatistics in 1998 from the University of Wisconsin-Madison. My PhD advisor was Murray Clayton.

I was a research scientist in the Statistical Data Analysis Center(SDAC) in the Department of Biostatistics and Medical Informatics at the University of Wisconsin-Madison, 1998-2005. I joined the faculty with a joint appointment in the Department of Biostatistics and Medical Informatics and the Department of Population Health Sciences in 2005.

I am an applied biostatistician focusing on problems in clinical and epidemiologic research. Current methodologic areas of interest include (1) multi-state models for incidence, progression and regression of ocular (and other) diseases, (2) small area estimation problems, particularly ranking, (3) spatial and spatio-temporal modeling, particularly cluster detection and high-dimensional variable selection and (4) age-period-cohort modeling.

Outside of the office, I’m an avid cyclist. You can check out my recent rides on Strava. I’m also a big movie fan. You’ll definitely see me at the Wisconsin Film Festival, one of my favorite events every year, and you can see lists of my favorite films and what I’ve been watching recently on Letterboxd.


  • Spatial and Spatio-Temporal Modeling
  • Age-Period-Cohort Models
  • Ranking
  • Multi-State Models


  • PhD in Statistics (emphasis in Biostatistics), 1998

    University of Wisconsin-Madison

  • MS in Statistics, 1994

    University of Wisconsin-Madison

  • BA in Mathematics and Economics, 1992

    University of Minnesota-Duluth


Breast Cancer Risk for Women with Diabetes and The Impact of Metformin – A Meta-analysis

Background: Diabetes mellitus has been associated with increased breast cancer (BC) risk; however, the magnitude of this effect is uncertain. This study focused on BC risk for women with type 2 diabetes mellitus (T2DM). Methods: Two separate meta-analyses were conducted to (1) estimate the relative risk (RR) of BC for women with T2DM and (2) to evaluate the risk of BC for women with T2DM associated with the use of metformin, a common diabetes treatment. In addition, subgroup analyses adjusting for obesity as measured by body-mass-index (BMI) and menopausal status were also performed. Studies were identified via PubMed/Scopus database and manual search through April 2021. Results: A total of 30 and 15 studies were included in the first and second meta-analysis, respectively. The summary RR of BC for women with T2DM was 1.15 (95% confidence interval (CI), 1.09-1.21). The subgroup analyses adjusting BMI and adjusting BMI and menopause resulted in a summary RR of 1.22 (95% CI, 1.15-1.30) and 1.20 (95% CI, 1.05-1.36), respectively. For women with T2DM, the summary RR of BC was 0.82 (95% CI, 0.60-1.12) for metformin users compared with non-metformin users. Conclusions: Women with T2DM were more likely to be diagnosed with BC and this association was strengthened by adjusting for BMI and menopausal status. No statistically significant reduction of BC risk was observed among metformin users. Impact: These two meta-analyses can inform decision-making for women with type 2 diabetes regarding their use of metformin and use of screening mammography for early detection of breast cancer.

Risk of COVID-19 Illness among In-Person K-12 School Educators and Its Association with School District Prevention Policies – Wisconsin, September 2-November 24, 2021

Objectives: To assess the rate of COVID-19 among in-person K-12 educators and its association with various COVID-19 prevention policies in school districts. Methods: Actively working, in-person K-12 educators in Wisconsin were linked to COVID-19 cases with onset during September 2–November 24, 2021. A mixed-effects Cox proportional hazards model, adjusted for pertinent person- and community-level confounders, compared the hazards rate of COVID-19 among educators working in districts with and without specific COVID-19 prevention policies. Results: In-person educators working in school districts that required masking for students and staff experienced 19% lower hazards of COVID-19 compared with those in districts without any masking policy (hazards ratio = 0.81, 95% confidence interval = 0.72 to 0.92). Reduced COVID-19 hazards were consistent and remained statistically significant when educators were stratified by elementary, middle, and high school environments. Conclusions: In Wisconsin’s K-12 school districts, during the Fall 2021 academic semester, a policy that required both students and staff to mask was associated with significantly reduced risk of COVID-19 among in-person educators across all grade levels.

Assessing the Relationship Between Physical Activity and the Gut Microbiome in a Large, Population-Based Sample of Wisconsin Adults

The gut microbiome is an important factor in human health and disease. While preliminary studies have found some evidence that physical activity is associated with gut microbiome richness, diversity, and composition, this relationship is not fully understood and has not been previously characterized in a large, population-based cohort. In this study, we estimated the association between several measures of physical activity and the gut microbiota in a cohort of 720 Wisconsin residents. Our sample had a mean age of 55 years (range: 18, 94), was 42% male, and 83% of participants self-identified as White. Gut microbial composition was assessed using gene sequencing of the V3-V4 region of the 16S rRNA extracted from stool. We found that an increase of one standard deviation in weekly minutes spent in active transportation was associated with an increase in alpha diversity, particularly in Chao1’s richness (7.57, 95% CI: 2.55, 12.59) and Shannon’s diversity (0.04, 95% CI: 0.0008, 0.09). We identified interactions in the association between Inverse Simpson’s diversity and physical activity, wherein active transportation for individuals living in a rural environment was associated with additional increases in diversity (4.69, 95% CI: 1.64, 7.73). We also conducted several permutational ANOVAs (PERMANOVA) and negative binomial regression analyses to estimate the relationship between physical activity and microbiome composition. We found that being physically active and increased physical activity time were associated with increased abundance of bacteria in the family Erysipelotrichaceae. Active transportation was associated with increased abundance of bacteria in the genus Phascolarctobacterium, and decreased abundance of Clostridium. Minutes in active transportation was associated with a decreased abundance of the family Clostridiaceae.

A flexible method for identifying spatial clusters of breast cancer using individual-level data

Prior research has shown that cancer risk varies by geography, but scan statistic methods for identifying cancer clusters in case-control studies have been limited in their ability to identify multiple clusters and adjust for participant-level risk factors. We develop a method to identify geographic patterns of breast cancer odds using the Wisconsin Women’s Health Study, a series of 5 population-based case-control studies of female Wisconsin residents aged 20-79 enrolled in 1988-2004 (cases=16,076, controls=16,795). We create sets of potential clusters by overlaying a 1 km grid over each county-neighborhood and enumerating a series of overlapping circles. Using a two-step approach, we fi=t a penalized binomial regression model to the number of cases and trials in each grid cell, penalizing all potential clusters by the least absolute shrinkage and selection operator (Lasso). We use BIC to select the number of clusters, which are included in a participant-level logistic regression model. We identify 15 geographic clusters, resulting in 23 areas of unique geographic odds ratios. After adjustment for known risk factors, confidence intervals narrowed but breast cancer odds ratios did not meaningfully change; one additional hotspot was identified. By considering multiple overlapping spatial clusters simultaneously, we discern gradients of spatial odds across Wisconsin.

Examining the Association between the Gastrointestinal Microbiome and Gulf War Illness: A Prospective Cohort Study

Gulf War Illness (GWI) affects approximately 25-35% of the 1991 Gulf War Veteran population. Those affected experience a wide range of symptoms including pain, fatigue, cognitive impairments, gastrointestinal dysfunction, skin disorders, and respiratory symptoms. Longitudinal studies have shown patients with GWI have had little to improvement in symptoms since their diagnosis. The gut microbiome and diet have been shown to play an important role in overall health and preliminary research has shown the gut microbiota may play a role in GWI as well. To examine the relationship between the gut microbiota, diet, and GWI, we conducted an eight week prospective cohort study collecting stool samples, medication and health history, and dietary data. Stool samples were analyzed using 16S rRNA sequencing on the Illumina MiSeq. Sixty-nine participants were enrolled into the study, 36 of which met the case definition for GWI. The gut microbiome of participants was very stable over the duration of the study and showed no within person (alpha diversity) differences between groups though beta diversity was statistically different between those with and without GWI. Several taxonomic lineages were identified as differentially abundant between those with and without GWI (n=9).