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
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.
Timing and Variability of Maternal Hyperglycemia in Insulin‐Dependent Diabetes, Long‐Term Effects on Offspring Obesity—The TEAM Study. 2025; 1.
EPS2.01Challenges in evaluating the Long-term effectiveness of cystic fibrosis modulator therapies after rapid and widespread adoption: a dual-approach study. Journal of Cystic Fibrosis. 2025; 24:s41-s42.
A Joint Model for (Un)Bounded Longitudinal Markers, Competing Risks, and Recurrent Events Using Patient Registry Data. Statistics in Medicine. 2025; 44:e70057.
Effects of azithromycin in young adults with cystic fibrosis: a protocol for emulating a published randomised controlled trial using registry data. BMJ Open. 2025; 15:e091357.
Characteristics of individuals with cystic fibrosis in the United States ineligible for ivacaftor and elexacaftor/tezacaftor/ivacaftor. Journal of Cystic Fibrosis. 2025; 24:255-262.
Spike and Slab Regression for Nonstationary Gaussian Linear Mixed Effects Modeling of Rapid Disease Progression. Environmetrics. 2025; 36.
Predicting lung function decline in cystic fibrosis: the impact of initiating ivacaftor therapy. Respiratory Research. 2024; 25:187.
Comparison of Longitudinal Outcomes in Children with Primary Ciliary Dyskinesia and Cystic Fibrosis. Annals of the American Thoracic Society. 2024; 21:1723-1732.
Robust identification of environmental exposures and community characteristics predictive of rapid lung disease progression. Science of the Total Environment. 2024; 950:175348.
Rhonda D. Szczesniak, PhD, Assem G. Ziady, PhD ...5/17/2021