Dr. Kurum is an Associate Professor of Biostatistics at the University of California, Riverside. Before joining UCR, she was a Postdoctoral Associate in the Department of Epidemiology of Microbial Diseases at Yale University. The main focus of her research is in the area of longitudinal and survival data analyses. Specifically, she is interested in building novel and flexible models that explore dynamic patterns, such as temporal dynamics, in the data and, thus, reveal the time-varying associations in real-life problems. Her research has been funded by external grants from National Institutes of Health and National Science Foundation. She was the recipient of a Fulbright Scholarship and the Kenneth Rothman Epidemiology Prize.
Talk: Joint Modeling of Interdependent Outcomes in Chronic Kidney Disease
Abstract: Nearly 15% (37 million) of adults in the U.S. have chronic kidney disease (CKD). This talk will cover two joint modeling approaches built to understand the mechanism and key risk factors underlying outcomes of interest for this patient population. The first methodology is a novel trivariate joint model, proposed to study the risk factors associated with the interdependent outcomes of kidney function (as measured by longitudinal estimated glomerular filtration rate), recurrent cardiovascular events, and eventual terminal event (kidney failure or mortality). The method is applied to study the aforementioned trivariate processes using data from the Chronic Renal Insufficiency Cohort Study, an ongoing prospective cohort study established to address the rising epidemic of CKD in the U.S. For the second joint modeling approach, we will focus on patients with end-stage kidney disease (ESKD; last stage of CKD). ESKD affected nearly 808,000 individuals as of 2020, with about 70% of patients on dialysis, a life-sustaining treatment. Dialysis patients experience high mortality and frequent hospitalizations, at about twice per year. These poor outcomes are exacerbated at key time periods, such as the fragile period after transition to dialysis. To study the time-varying effects of modifiable patient and dialysis facility risk factors on the correlated outcomes, hospitalization and mortality, we propose a novel multilevel time-varying joint model. Application to the United States Renal Data System, a national database that contains data on nearly all patients on dialysis in the United States, highlights significant time-varying effects of patient- and facility-level risk factors on hospitalization risk and mortality.