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Mapping cancer risk in southwestern Ontario with changing census boundaries. (English) Zbl 1259.62099
Summary: Mapping disease risk often involves working with data that have been spatially aggregated to census regions or postal regions, either for administrative reasons or confidentiality. When studying rare diseases, data must be collected over a long time period in order to accumulate a meaningful number of cases. These long time periods can result in spatial boundaries of the census regions changing over time, as is the case with the motivating example of exploring the spatial structure of mesothelioma lung cancer risk in Lambton County and Middlesex County of southwestern Ontario, Canada.
This article presents a local-EM kernel smoothing algorithm that allows for the combining of data from different spatial maps, being capable of modeling risk for spatially aggregated data with time-varying boundaries. Inference and uncertainty estimates are carried out with parametric bootstrap procedures, and cross-validation is used for bandwidth selection. Results for the lung cancer study are shown and discussed.
Reviewer: Reviewer (Berlin)
62P10 Applications of statistics to biology and medical sciences; meta analysis
92C50 Medical applications (general)
62H11 Directional data; spatial statistics
62F40 Bootstrap, jackknife and other resampling methods
65C60 Computational problems in statistics (MSC2010)
R; Rmpi
Full Text: DOI
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