Multilevel modeling of spatially nested functional data: spatiotemporal patterns of hospitalization rates in the U.S. dialysis population
Damla Senturk, PhD University of California at Los Angeles
End-stage renal disease patients on dialysis experience frequent hospitalizations. In
addition to known temporal patterns of hospitalizations over the life span on dialysis,
where poor outcomes are typically exacerbated during the rst year on dialysis, variations
in hospitalizations among dialysis facilities across the U.S. contribute to spatial variation.
Utilizing national data from the United States Renal Data System (USRDS), we propose
a novel multilevel spatiotemporal functional model to study spatiotemporal patterns of
hospitalization rates among dialysis facilities. Hospitalization rates of dialysis facilities
are considered as spatially nested functional data with longitudinal hospitalizations
nested in dialysis facilities and dialysis facilities nested in geographic regions. A multilevel
Karhunen-Loeve expansion is utilized to model the two-level (facility and region)
functional data, where spatial correlations are induced among region-specific principal
component scores accounting for regional variation. A new efficient algorithm based on
functional principal component analysis and Markov Chain Monte Carlo is proposed for
estimation and inference. We report a novel application using USRDS data to characterize
spatiotemporal patterns of hospitalization rates for over 400 health service areas
across the U.S. and over the post-transition time on dialysis. Finite sample performance
of the proposed method is studied through simulations.