Dr. Xiangrong Kong is on faculty at the Dana Center for Preventive Ophthalmology, and holds joint appointments in the Departments of Biostatistics, Epidemiology, and Health, Behavior and Society in the Bloomberg School of Public Health. Dr. Kong received her PhD in Biostatistics from the Medical College of Virginia at the Virginia Commonwealth University in 2008. She then joined the Johns Hopkins Bloomberg School of Public Health as a research faculty member, and received further training in global health, infectious disease epidemiology, and behavioral sciences.
Dr. Kong is director and biostatistician of the Data Coordinating Center of the international multi-center natural history study on the Progression of Atrophy Secondary to Stargardt Disease (ProgStar). She conducts research at the interface of biostatistics and ophthalmology, and has worked on a broad range of clinical ophthalmic studies, including genetic retinal degeneration, ocular neuroretinal measurements, and low vision rehabilitation.
A main theme of Dr. Kong’s current work concerns statistical and quantitative approaches for understanding how imperfectly measured ocular parameters using modern imaging technologies (e.g. OCT, fundus autofluorescence) will impact on inference of disease progression and of structural and functional relationships. The better understanding is necessary for determining appropriate structural parameters for future clinical trials, especially for diseases with slow functional decline, facilitating bench-to-bedside translations.
In other areas, Dr. Kong has worked on program impact evaluation for assessing the population level impact of scale-up of HIV preventive interventions on the HIV epidemic in South Uganda. She also has training and research experience in developing, evaluating and implementing social and behavioral sciences approaches for promoting health behaviors. Dr. Kong’s research interests in biostatistical methodology include correlated data analysis, longitudinal data analysis, survival analysis, measurement error models, and power and sample size calculations.