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In This Section
Dr. Zee's statistical methods research includes topics in survival analysis, measurement error, observational data methods, and machine learning. Her clinical research is focused on kidney disease, specifically in clinical and pathology markers of glomerular and chronic kidney disease progression.
Dr. Jarcy Zee is an assistant professor of Biostatistics at Children's Hospital of Philadelphia and the Department of Biostatistics, Epidemiology, and Informatics at the Perelman School of Medicine of the University of Pennsylvania. Her statistical methods interests are in survival analysis, measurement error, observational data analysis methods, and machine learning. Her clinical research is focused primarily on kidney disease, with specific interests in glomerular disease and chronic kidney disease.
Dr. Zee's current research projects include investigation of surrogate endpoints for kidney disease progression, agreement measures for assessing inter-rater reproducibility, methods for identifying time-varying and high-dimensional biomarkers of time-to-event outcomes, and the application of computational pathology for kidney biopsy image analysis.
Education and Training
BS, University of Delaware (Mathematics and Economics), 2007
MS, University of Pennsylvania (Biostatistics), 2012
PhD, University of Pennsylvania (Biostatistics), 2014
Titles and Academic Titles
Assistant Professor of Biostatistics
Senior Scholar, Center for Clinical Epidemiology and Biostatistics
Faculty Member, Graduate Group in Epidemiology and Biostatistics
American Statistical Association, 2011-
Eastern North American Region, International Biometric Society, 2012-
American Society of Nephrology, 2014-
American Statistical Association Biometrics Section Student Travel Award, 2014
Saul Winegrad, MD, Award for Outstanding Dissertation in Biostatistics, 2015
Rare Disease Clinical Research Travel Award, 2016
Causal Effects of Time-Varying Exposures on Recurrent Outcomes With Time-Dependent Confounding
NephCure Kidney International
PIs: Jarcy Zee, PhD
In this pilot project, we are developing a marginal structural proportional rates model to estimate the effect of a time-varying exposure on recurrent event outcomes in the presence of time-dependent confounding. The model will be applied to data from the Cure Glomerulonephropathy (CureGN) study to estimate the causal effect of time-varying steroid use on recurrent infections and time-varying RAAS inhibitor use on recurrent proteinuria remissions.
Computational Pathology for Proteinuric Glomerulopathies
2021 - 2026
PIs: Laura Barisoni, MD, Jarcy Zee, PhD, Jeffrey Hodgin, MD, PhD, and Lawrence Holzman, MD
In this project, we are using deep learning and other computational approaches to derive histologic and/or pathomic features from kidney biopsy images. We then integrate pathology data with clinical outcomes and molecular data to identify novel imaging markers that are both biologically and clinically relevant. Finally, we will develop machine learning models that integrate data domains for predicting clinical outcomes.
Novel Outcomes and Machine Learning Approaches for Improving Prediction of CKD Progression
NIH/NIDDK Chronic Renal Insufficiency Cohort (CRIC) Study Opportunity Pool
2020 - 2021
PIs: Jarcy Zee, PhD, and Abigail Smith, PhD (Parent Study PI: Harold Feldman, MD, MSCE)
Disease progression outcomes are evaluated against kidney failure to determine the optimal progression outcome. We also assess the effect of non-linear eGFR trajectories on the ability of progression outcomes to predict kidney failure. We will compare machine learning approaches for building risk prediction models of CKD progression, including assessment of model building techniques.
Continuation of the Nephrotic Syndrome Rare Disease Clinical Research Network (NEPTUNE)
2019 - 2024
PI: Matthias Kretzler, MD
We leverage the NEPTUNE resources to catalyze discovery, training and outreach as we strive to improve health outcomes for individuals affected by nephrotic syndrome (NS). The overarching goal is to apply a precision medicine approach to NS, leveraging the extensive NEPTUNE Knowledge Network established since 2009. NEPTUNE will implement this strategy to permit discovery of novel therapeutic targets and deploy a patient stratification approach to help identify the right trial for the right patient at the right time. Training, pilot and ancillary study programs will continue with significant funding support from Nephcure Kidney International.