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.
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
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.
Spatial and spatio-temporal cluster detection are important tools in public health and many other areas of application. Cluster detection can be approached as a multiple testing problem, typically using a space and time scan statistic. We recast the spatial and spatio-temporal cluster detection problem in a high-dimensional data analytical framework with Poisson or quasi-Poisson regression with the Lasso penalty. We adopt a fast and computationally-efficient method using a novel sparse matrix representation of the effects of potential clusters. The number of clusters and tuning parameters are selected based on (quasi-)information criteria. We evaluate the performance of our proposed method including the false positive detection rate and power using a simulation study. Application of the method is illustrated using breast cancer incidence data from three prefectures in Japan.
Background: Recent reports suggest that racial differences in breast cancer incidence rates have decreased. We examined whether these findings apply to breast cancer mortality while considering age, period, and cohort influences on both absolute and relative measures of breast cancer mortality. Methods: Using publicly available datasets (CDC Wonder, Human Mortality Database), we developed an age-period-cohort model of breast cancer mortality and breast cancer deaths as a proportion of all deaths during 1968-2019 among all women and by five race/ethnicity groups with sufficient numbers for estimation: Hispanic (all races), American Indian/Alaska Native and Asian/Pacific Islanders (regardless of ethnicity), non-Hispanic Black, and non-Hispanic White. Results: Initially increasing after 1968, age-adjusted breast cancer mortality rates have decreased among all racial/ethnic groups since 1988. The age-adjusted percent of all deaths due to breast cancer also has been declining for non-Hispanic White women since about 1990 while increasing or holding steady for other race/ethnic groups. In 2019, the age-adjusted percent of deaths due to breast cancer for women was highest for Asian/Pacific Islanders (5.6%) followed by non-Hispanic Black (4.5%), Hispanic (4.4%), non-Hispanic White (4.1%), and American Indian/Alaska Native women (2.6%). Conclusions: Breast cancer mortality disparities are now greater on both relative and absolute scales for non-Hispanic Black women, and using the relative scale for Asian/Pacific Islanders and Hispanic women, compared with non-Hispanic White women for the first time in 50 years.
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.
It is fairly common to rank different geographic units, e.g. counties in the USA, based on health indices. In a typical application, point estimates of the health indices are obtained for each county, and the indices are then simply ranked as if they were known constants. Several authors have considered optimal rank estimators under squared error loss on the rank scale as a default method for general purpose ranking, e.g. situations where ranking units across the full spectrum of performance (low, medium, high) is important. While computationally convenient, squared error loss on the rank scale may not represent the true inferential goals of rank consumers. We construct alternative loss functions based on three components: (1) the inferential goal (rank position or pairwise comparisons), (2) the scale (original, log-transformed or rank) and (3) the (positional or pairwise) loss function (0/1, squared error or absolute error). We can obtain optimal ranks for loss functions based on rank positions and nearly optimal ranks for loss functions based on pairwise comparisons paired with highest posterior density (HPD) credible intervals. We compare inferences produced by the various ranking methods, both optimal and heuristic, using low birth weight data for counties in the Midwestern United States, from 2006 to 2012.
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.
Purpose To identify cognitive, behavioral, environmental, and other factors that influence physical activity in adults with advanced cancer using qualitative, semi-structured interviews. Methods Eighteen semi-structured interviews were conducted with adults living with stage IV breast, prostate, or colorectal cancer; or multiple myeloma recruited from the University of Wisconsin Carbone Cancer Center. We used the Social Cognitive Theory to design the interview guide and a reflexive thematic approach for analysis. Results Participants were 62 years old on average and currently receiving treatment. Despite reporting numerous barriers to physical activity, most participants discussed engaging in some physical activity. Participants reported difficulties coping with changes in physical functioning especially due to fatigue, weakness, neuropathy, and pain. While cold weather was seen as a deterrent for activity, access to sidewalks was a commonly reported feature of neighborhood conduciveness for physical activity. Regardless of current activity levels, adults with advanced cancer were interested in engaging in activities to meet their goals of gaining strength and maintaining independence. Having a conversation with a provider from their cancer care team about physical activity was seen as encouraging for pursuing some activity. Conclusions Adults living with advanced cancer are interested in pursuing activity to gain strength and maintain independence despite reported barriers to physical activity. To ensure patients are encouraged to be active, accessible resources, targeted referrals, and interventions designed to address their goals are critical next steps. Relevance Integrating conversations about physical activity into oncology care for adults living with advanced cancer is an important next step to encourage patients to remain active and help them improve strength and maintain quality of life and independence.
Objectives To characterize the effect of the actual and potential ability to get rides from others on older adults’ driving reduction at three-year follow up in the United States. Methods We analyzed National Health and Aging Trends Study data from community-dwelling drivers in 2015 (unweighted n = 5,102). We used weighted logistic regression models to estimate whether getting rides from others in 2015 was associated with older adults increasing the number of driving behaviors they avoided, decreasing the frequency with which they drove, or not driving at three-year follow up after adjusting for biopsychosocial variables. We also measured presence of social network members living nearby including household and non-household members and estimated associated odds of driving reduction at three-year follow up. Results Older adults who got rides from others in 2015 had greater odds of reporting no longer driving at three-year follow up compared to those who did not get rides (adjusted Odds Ratio [aOR] = 1.53, 95% Confidence Interval [CI]: 1.11-2.11). We found no statistically significant association between older adults living with others or having more nearby confidantes outside their household and their odds of reducing driving at three-year follow up. Discussion These findings suggest that getting rides from others plays an important role in the transition to non-driving for older adults. Future research should examine whether other aspects of social networks (e.g., type, quality, closer proximity) might also be key modifiable coping factors for older adults transitioning to non-driving.
Background: The atopic march refers to the co-expression and progression of atopic diseases in childhood, often beginning with atopic dermatitis, although children may not progress through each atopic disease. Objective: We hypothesized that future atopic disease expression is modified by atopic dermatitis phenotype, and that these differences result from underlying dysregulation of cytokine signaling. Methods: Children (n=285) were enrolled into the Childhood Origins of ASThma birth cohort and followed prospectively. Rates of atopic dermatitis, food allergy, allergic rhinitis, and asthma were assessed longitudinally from birth to 18 years of age. Associations between atopic dermatitis phenotype and food allergy, allergic rhinitis, asthma, allergic sensitization, exhaled nitric oxide, and lung function were determined. Peripheral blood mononuclear cell responses (IL-5, IL-10, IL-13, IFN-γ) to dust mite, phytohemagglutinin, Staphylococcus aureus Cowan I, and tetanus toxoid were compared among atopic dermatitis phenotypes. Results: Atopic dermatitis at year 1 was associated with an increased risk of food allergy (p=0.004). Both persistent and late-onset atopic dermatitis were associated with an increased risk of asthma (p=<0.001), rhinitis (p<0.001), elevated total IgE (p=<0.001), percentage of aeroallergens with detectable IgE (p<0.001), and elevated exhaled nitric oxide (p=0.002). Longitudinal analyses did not reveal consistent differences in PBMC responses among dermatitis phenotypes. Conclusion: Atopic dermatitis phenotype is associated with differential expression of other atopic diseases. Our findings suggest peripheral blood cytokine dysregulation is not a mechanism underlying this process, and immune dysregulation may be mediated at mucosal surfaces or in secondary lymphoid organs. Keywords: Atopic dermatitis; allergic sensitization; atopic dermatitis phenotypes; atopic march; progression of atopic disease.
Background: Farm exposures in early life reduce the risks for childhood allergic diseases and asthma. There is less information about how farm exposures relate to respiratory illnesses and mucosal immune development. Objective: We hypothesized that children raised in farm environments have a lower incidence of respiratory illnesses over the first two years of life than non-farm children. We also analyzed whether farm exposures or respiratory illnesses were related to patterns of nasal cell gene expression. Methods: The Wisconsin Infant Study Cohort included farm (n=156) and non-farm (n=155) families with children followed to age 2 years. Parents reported prenatal farm and other environmental exposures. Illness frequency and severity were assessed using illness diaries and periodic surveys. Nasopharyngeal cell gene expression in a subset of 64 children at age two years was compared to farm exposure and respiratory illness history. Results: Farm vs. non-farm children had nominally lower rates of respiratory illnesses (rate ratio 0.82 [0.69,0.97]) with a stepwise reduction in illness rates in children exposed to 0, 1, or ≥2 animal species, but these trends were non-significant in a multivariable model. Farm exposures and preceding respiratory illnesses were positively related to nasal cell gene signatures for mononuclear cells and innate and antimicrobial responses. Conclusions: Maternal and infant exposure to farms and farm animals was associated with nonsignificant trends for reduced respiratory illnesses. Nasal cell gene expression in a subset of children suggests that farm exposures and respiratory illnesses in early life are associated with distinct patterns of mucosal immune expression. Keywords: Farm; children; gene expression; nasal epithelial cells; respiratory illness; virus.