||5R03CA078169-02 Interpret this number
||Iowa State University
||Inference for Extremes in Disease Maps of Small Areas
DESCRIPTION (Applicant's Description) Disease-incidence or disease-mortality
data are often reported for small areas and displayed using a choropleth
map, e.g., in cancer atlases. Areas with extremely high incidence rates may
be subject to further scrutiny in an attempt to identify possible risk
factors. Case-control studies may then be carried out to estimate the
effects of a particular risk factor. This research program is intended to
explore, over the long term, a number of statistical issues related to the
display, analysis, and interpretation of disease-incidence or
disease-mortality data. The specific aims of this small grant application
include improved small-area estimation with a focus on identifying regions
with extreme rates, and identification of the regions, if any, that exhibit
rates beyond what might be expected due to chance fluctuations. It is
expected that this research will lead into building point-level
epidemiological mechanisms into a model for small-area data, and the
visualization of data and results using a Geographic Information System.
The primary research tool for the goals of this application is a Bayesian
hierarchical model for small-area disease incidence data. Three approaches
to inference for extreme values are to be investigated: inference directly
from the posterior distribution of small-area disease rates, constrained
Bayesian estimation using squared-error loss, and Bayesian estimation based
on loss functions emphasizing extrema.
Posterior predictive model checks for disease mapping models.
, Cressie N.
Statistics in medicine, 2000 Sep 15-30; 19(17-18), p. 2377-97.