Statistics Colloquium
October 10 (Fri) 11 a.m.
Alavi Commons Room, 6625 Everett Tower

A Spatiotemporal Quantile Regression Model for Emergency Department Expenditures

Brian Neelon, PhD
Department of Biostatistics and Bioinformatics
Duke University School of Medicine

Motivated by a recent study of geographic and temporal trends in emergency department (ED) care, we develop a spatiotemporal quantile regression model for the analysis of ED-related medical expenditures. The model yields distinct spatial patterns across time for each quantile of the response distribution, which is important in the spatial analysis of expenditures, as there is often little spatiotemporal variation in mean expenditures but more pronounced variation in the extremes. The model has a hierarchical structure incorporating patient- and region-level predictors as well as spatiotemporal random effects. We model the random effects via intrinsic conditionally autoregressive priors, improving small-area estimation through maximum spatiotemporal smoothing. We adopt a Bayesian modeling approach based on an asymmetric Laplace distribution and develop an efficient posterior sampling scheme that relies solely on conjugate full conditionals. We apply our model to data from the Duke Support Repository, a large georeferenced database containing health and financial data for Duke Health System patients residing in Durham County, North Carolina. `

All statistics students are expected to attend.


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Department of Statistics
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Western Michigan University
Kalamazoo MI 49008-5152 USA
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