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


Cluster Analysis Methods to Support Population Health Improvement among U.S. Counties

Context: Population health rankings can be a catalyst for the improvement of health by drawing attention to areas in need of relative improvement and summarizing complex information in a manner understood by almost everyone. However, ranks also have unintended consequences, such as being interpreted as “hard truths”, where variations may not be significant. There is a need to improve communication about uncertainty in ranks, with accurate interpretation. The most common solutions discussed in the literature have included modeling approaches to minimize statistical noise or borrow strength from covariates. However, the use of complex models can limit communication and implementation, especially for broad audiences. Objectives: Explore data-informed grouping (cluster analysis) as an easier-to-understand, empirical technique to account for rank imprecision that can be effectively communicated both numerically and visually. Design: Cluster analysis, specifically k-means clustering with Wasserstein (earth mover’s) distance, was explored as an approach to identify natural and meaningful groupings and gaps in the data distribution for the County Health Rankings’ (CHR) health outcomes ranks. Setting: County-level health outcomes from the 2022 CHR Participants: 3,082 counties that were ranked in the 2022 CHR Main Outcome Measure: Data-informed health groups Results: Cluster analysis identified 30 health groupings among counties nationwide, with cluster size ranging from nine to 184 counties. On average, states had 16 identified clusters, ranging from 3 in Delaware and Hawaii to 27 in Virginia. Number of clusters per state was associated with number of counties per state and population of the state. The method helped address many of the issues that arise from providing rank estimates alone. Conclusions: Public health practitioners can use this information to understand uncertainty in ranks, visualize distances between county ranks, have context around which counties are not meaningfully different from one another, and compare county performance to peer counties.

Social and Environmental Characteristics Associated With Older Drivers’ Use of Non-driving Transportation Modes

Objective: We examined associations between older drivers’ social and environmental characteristics and odds of using non-driving transportation modes. Methods: Using 2015 National Health and Aging Trends Study data for community-dwelling drivers (n = 5102), we estimated logistic regression models of associations between social characteristics, environmental characteristics, and odds of using non-driving transportation modes three years later. Results: Drivers had 20% increase in odds of getting rides three years later for each additional confidante (adjusted odds ratio [aOR] = 1.20, 95% confidence interval [CI]: 1.11–1.30). Drivers living in more walkable neighborhoods were more likely to walk to get places (National Walkability Index [NWI] score of 18 vs. 2 aOR = 1.71, 95% CI: 1.02–2.90) and take public transit three years later (NWI 18 vs. 2 aOR = 7.47, 95% CI: 1.69–33.0). Discussion: Identifying modifiable social and environmental characteristics can inform future interventions supporting older adults’ health during the transition to non-driving.

Collaborative Modeling to Compare Different Breast Cancer Screening Strategies: A Decision Analysis for the US Preventive Services Task Force

IMPORTANCE The effects of breast cancer incidence changes and advances in screening and treatment on outcomes of different screening strategies are not well known. OBJECTIVE To estimate outcomes of various mammography screening strategies. DESIGN, SETTING, AND POPULATION Comparison of outcomes using 6 Cancer Intervention and Surveillance Modeling Network (CISNET) models and national data on breast cancer incidence, mammography performance, treatment effects, and other-cause mortality in US women without previous cancer diagnoses. EXPOSURES Thirty-six screening strategies with varying start ages (40, 45, 50 years) and stop ages (74, 79 years) with digital mammography or digital breast tomosynthesis (DBT) annually, biennially, or a combination of intervals. Strategies were evaluated for all women and for Black women, assuming 100% screening adherence and “real-world” treatment. MAIN OUTCOMES AND MEASURES Estimated lifetime benefits (breast cancer deaths averted, percent reduction in breast cancer mortality, life-years gained), harms (false-positive recalls, benign biopsies, overdiagnosis), and number of mammograms per 1000 women. RESULTS Biennial screening with DBT starting at age 40, 45, or 50 years until age 74 years averted a median of 8.2, 7.5, or 6.7 breast cancer deaths per 1000 women screened, respectively, vs no screening. Biennial DBT screening at age 40 to 74 years (vs no screening) was associated with a 30.0%breast cancer mortality reduction, 1376 false-positive recalls, and 14 overdiagnosed cases per 1000 women screened. Digital mammography screening benefits were similar to those for DBT but had more false-positive recalls. Annual screening increased benefits but resulted in more false-positive recalls and overdiagnosed cases. Benefit-to-harm ratios of continuing screening until age 79 years were similar or superior to stopping at age 74. In all strategies, women with higher-than-average breast cancer risk, higher breast density, and lower comorbidity level experienced greater screening benefits than other groups. Annual screening of Black women from age 40 to 49 years with biennial screening thereafter reduced breast cancer mortality disparities while maintaining similar benefit-to-harm trade-offs as for all women. CONCLUSIONS This modeling analysis suggests that biennial mammography screening starting at age 40 years reduces breast cancer mortality and increases life-years gained per mammogram. More intensive screening forwomen with greater risk of breast cancer diagnosis or death can maintain similar benefit-to-harm trade-offs and reduce mortality disparities.

COVID-19 Pandemic Impacts on Behavioral and Emotional Health of Young Children with Autism

Objective To test our initial hypotheses that the COVID-19 pandemic was associated with: (1) decreases in adaptive behavior and increases in behavioral and emotional problems of children with autism; (2) greater impacts for those who lost specialty services; and (3) greater behavioral and emotional problems for children with autism versus control participants. Method Eligible participants (N=1,158) enrolled in Phase 3 of the multi-site, case-control Study to Explore Early Development (SEED) prior to March 31, 2020, between 2-5 years old and completed follow-up assessments between January-July 2021. Caregivers completed a COVID-19 Impact Assessment Questionnaire, Vineland Adaptive Behavior Scales (VABS), and Child Behavior Checklist (CBCL) for 274 children with autism and 385 control participants. Results Mean VABS communication scores of children with autism decreased significantly (-4.2; standard deviation [SD], 10.5) between pre-pandemic and pandemic periods, while VABS composite (+2.0; SD, 9.0), daily living (+5.5; SD, 11.4), socialization (+2.3; SD, 10.0), and CBCL scores (-3.2; SD, 8.4) improved. In contrast, CBCL scores worsened in population control participants (+3.4; SD, 8.8). Children with autism who missed specialty appointments scored significantly lower on the VABS during the pandemic versus those who did not (VABS Composite 70.6; 95% confidence interval [CI]: 68.8-72.4 vs. 74.5; 95% CI: 71.8-77.2). Conclusion While stay-at-home policies of the pandemic may have beneficially impacted daily living skills, socialization, and behavioral and emotional wellbeing of children with autism, benefits may have occurred at the cost of communication skills. These findings indicate the need for strategies to maintain therapeutic services in future emergency settings.