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.


  • 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


The Association Between Exposure to Maternal Depression During Year 2 of a Child's Life and Future Child Problem Behavior

Objectives: We examined the association of exposure to maternal depression when a child is age 3 with future child problem behavior and investigated whether race/ethnicity is a moderator of this relationship. Methods: We used Fragile Families and Child Well-Being Study data (age 3 N=3288 and 49% Black, 26% Hispanic, 22% non-Hispanic White; age 5 N=3001 and 51% Black, 25% Hispanic, 21% non-Hispanic White; age 9 N=3630 and 50% Black, 25% Hispanic, 21% non-Hispanic White) and ordinal logistic regression to model problem behavior at ages 3, 5, and 9 on maternal depression status at age 3. Results: At age 9, children whose mother was depressed when the child was age 3 were significantly more likely to have higher internalizing (AOR=1.92, 95% CI: 1.42,2.61) and externalizing (AOR=1.65, 95% CI: 1.10,2.48) problem behavior scores. Race/ethnicity did not have moderating effects, potentially due to a limitation of the data that required use of maternal self-reported race/ethnicity as a proxy for child race/ethnicity. Conclusions for Practice: Exposure to maternal depression after the prenatal and perinatal periods may have a negative association with children’s behavioral development through age 9. Interventions that directly target maternal depression during this time should be developed. Additional research is needed to further elucidate the role of race/ethnicity in the relationship between maternal depression and child problem behavior.

Clustered Spatio-Temporal Varying Coefficient Regression Model

In regression analysis for spatio-temporal data, identifying clusters of spatial units over time in a regression coefficient could provide insight into the unique relationship between a response and covariates in certain subdomains of space and time windows relative to the background in other parts of the spatial domain and the time period of interest. In this article, we propose a varying coefficient regression method for spatial data repeatedly sampled over time, with heterogeneity in regression coefficients across both space and over time. In particular, we extend a varying coefficient regression model for spatial-only data to spatio-temporal data with flexible temporal patterns. We consider the detection of a potential cylindrical cluster of regression coefficients based on testing whether the regression coefficient is the same or not over the entire spatial domain for each time point. For multiple clusters, we develop a sequential identification approach. We assess the power and identification of known clusters via a simulation study. Our proposed methodology is illustrated by the analysis of a cancer mortality dataset in the Southeast of the U.S.

Developmental patterns in the nasopharyngeal microbiome during infancy are associated with asthma risk

Background: Studies indicate that the nasal microbiome may correlate strongly with the presence or future risk of childhood asthma. Objectives: In this study, we tested whether developmental trajectories of the nasopharyngeal microbiome in early life and the composition of the microbiome during illnesses were related to risk of childhood asthma. Methods: Children participating in the Childhood Origins of Asthma study (n=285) provided nasopharyngeal mucus samples in the first two years of life, during routine healthy study visits (2, 4, 6, 9, 12, 18 and 24 months of age) and episodes of respiratory illnesses, which were analyzed for respiratory viruses and bacteria. We identified developmental trajectories of early-life microbiome composition, as well as predominant bacteria during respiratory illnesses, and correlated these with presence of asthma at 6, 8, 11, 13 and 18 years of age. Results: Of the four microbiome trajectories identified, a Staphylococcus-dominant microbiome in the first 6 months of life was associated with increased risk of recurrent wheezing by age 3 years and asthma that persisted throughout childhood. In addition, this trajectory was associated with the early onset of allergic sensitization. During wheezing illnesses, detection of rhinoviruses and predominance of Moraxella were associated with asthma that persisted throughout later childhood. Conclusion: In infancy, the developmental composition of the microbiome during healthy periods and the predominant microbes during acute wheezing illnesses are both associated with the subsequent risk of developing persistent childhood asthma.

Accuracy of Wearable Trackers for Measuring Moderate- to Vigorous-Intensity Physical Activity: A Systematic Review and Meta-Analysis

Background: The evidence base regarding validity of wearable fitness trackers for assessment and/or modification of physical activity behavior is evolving. Accurate assessment of moderate- to vigorous-intensity physical activity (MVPA) is important for measuring adherence to physical activity guidelines in the United States and abroad. Therefore, this systematic review synthesizes the state of the validation literature regarding wearable trackers and MVPA. Methods: A systematic search of the PubMed, Scopus, SPORTDiscus, and Cochrane Library databases was conducted through October 2019 (PROSPERO Q1 registration number: CRD42018103808). Studies were eligible if they reported on the validity of MVPA and used devices from Fitbit, Apple, or Garmin released in 2012 or later or available on the market at the time of review. A meta-analysis was conducted on the correlation measures comparing wearables with the ActiGraph. Results: Twenty-two studies met the inclusion criteria; all used a Fitbit device; one included a Garmin model and no Apple-device studies were found. Moderate to high correlations (.7–.9) were found between MVPA from the wearable tracker versus criterion measure (ActiGraph n = 14). Considerable heterogeneity was seen with respect to the specific definition of MVPA for the criterion device, the statistical techniques used to assess validity, and the correlations between wearable trackers and ActiGraph across studies. Conclusions: There is a need for standardization of validation methods and reporting outcomes in individual studies to allow for comparability across the evidence base. Despite the different methods utilized within studies, nearly all concluded that wearable trackers are valid for measuring MVPA.

Effect of a Technology-Supported Physical Activity Intervention on Health-Related Quality of Life and Sleep in Cancer Survivors: A Randomized, Controlled Trial

Objectives This pilot trial tested the effect of adding a multi‐level, technology‐based physical activity intervention module to a standard survivorship care plan for breast and colorectal cancer survivors. The objective of this analysis was to determine whether the physical activity module improved health‐related quality of life, sleep, and factors key to lasting behavior change (eg, social support, self‐efficacy). Methods Breast and colorectal cancer survivors (n = 50) were enrolled alongside a support partner. Survivors were assigned to receive a standard survivorship care plan either alone or augmented by a 12‐week multi‐component physical activity module. The module included a Fitbit tracker (with the physical activity data integrated into the electronic health record for clinician review) and customized email feedback. Physical activity was measured using the ActiGraph GT3X+. Psychosocial outcomes included the SF‐36, FACT, ISEL, PROMIS sleep measures, and physical activity beliefs. Data were analyzed using linear mixed modeling. Results Cancer survivors were aged 54.4 ± 11.2 years and were 2.0 ± 1.5 years from diagnosis. Relative to comparison, the intervention was associated with moderate‐to‐large improvements in physical health (effect size: d = 0.39, 95% CI = 0.0, 0.78), mental health (d = 0.59, 95% CI = 0.19, 0.99), sleep impairment (d = 0.62, 95% CI = −1.02, −0.22), and exercise self‐efficacy (d = 0.60, 95% CI = 0.20, 1.0). Conclusions The intervention delivered meaningful improvements in survivors’ quality of life, social support, and sleep impairment. If replicated in a larger sample, adding a technology‐supported physical activity module to survivorship care plans may be a practical strategy for supporting healthy survivorship.