My research interests include predictive modeling, data management / coordination and medical monitoring, lung diseases and disorders, biomarker discovery and longitudinal data analysis. In my research lab, the goals of my team include designing and analyzing medical monitoring investigations as well as incorporating geo- and bio-markers for customized, enhanced prediction / early detection of swift disease progression.
Some of the most notable discoveries made at my lab include identifying pediatric phenotypes of rapid lung disease progression using the U.S. Cystic Fibrosis Registry and the geo- and bio-marker-informed prediction modeling of rapid lung disease progression.
I was led to my research interests by witnessing how certain things change over time and determining why things transform. This is why I pursued statistics in my graduate studies at the University of Kentucky.
As my career progressed, I received a recognition for biostatistical contributions to cystic fibrosis research in the Journal of Cystic Fibrosis in April 2019. I have also held reviewer positions on grant award panels, and I hold a membership in the Cystic Fibrosis Foundation Patient Registry / Comparative Effectiveness Research Committee. My research has been supported by the National Institutes of Health (NIH), the Cystic Fibrosis Foundation and the LAM Foundation.
I have more than 15 years of experience in the biostatistics field, and I first started working at the Cincinnati Children’s Hospital Medical Center in 2007. Lastly, my research work has been published in a multitude of journals, including Statistical Methods in Medical Research, Statistics in Medicine, Journal of Religion and Health, Journal of Cystic Fibrosis, Journal of Diabetes Research, Annals of the American Thoracic Society, Chest, and American Journal of Respiratory and Critical Care Medicine.
PhD: Statistics, University of Kentucky, Lexington, KY, 2007.
MS: Statistics, University of Kentucky, Lexington, KY, 2005.
BS: Mathematics, Radford University, Radford, VA, 2003.
Cystic fibrosis; blood pressure; glycemic control
Functional data analysis; longitudinal data analysis; medical monitoring; prediction
Biostatistics and Epidemiology
Multilevel joint model of longitudinal continuous and binary outcomes for hierarchically structured data. Statistics in Medicine. 2023; 42:2914-2927.
Lung Function Decline in Cystic Fibrosis: Impact of Data Availability and Modeling Strategies on Clinical Interpretations. Annals of the American Thoracic Society. 2023; 20:958-968.
Automated, machine learning-based alerts increase epilepsy surgery referrals: A randomized controlled trial. Epilepsia. 2023; 64:1791-1799.
Lung function and secondhand smoke exposure among children with cystic fibrosis: A Bayesian meta-analysis. Journal of Cystic Fibrosis. 2023; 22:694-701.
P185 Prospective randomized observational study validating biomarkers for association with future pulmonary exacerbations in people with cystic fibrosis. Journal of Cystic Fibrosis. 2023; 22:s121-s122.
Predicting Individualized Lung Disease Progression in Treatment-Naive Patients With Lymphangioleiomyomatosis. Chest. 2023; 163:1458-1470.
Built environment factors predictive of early rapid lung function decline in cystic fibrosis. Pediatric Pulmonology. 2023; 58:1501-1513.
115. Automated Identification of Transgender and Gender Non-conforming patients from Electronic Health Record Data. Journal of Adolescent Health. 2023; 72:s66.
Body composition and functional correlates of CF youth experiencing pulmonary exacerbation and recovery. Pediatric Pulmonology. 2023; 58:457-464.
Real-world Associations of US Cystic Fibrosis Newborn Screening Programs With Nutritional and Pulmonary Outcomes. JAMA Pediatrics. 2022; 176:990-999.