Spatial Uncertainty and Hypothesis Testing in Medical Geography: a Geostatistical Perspective

Pierre Goovaerts
BioMedware, Inc Ann Arbor, Michigan, USA
goovaerts@biomedware.com

Abstract.This paper provides an overview of geostatistical methods available for the analysis of both individual-level and aggregated health outcomes, with applications to cancer mortality and incidence in the US. Traditional kriging and stochastic simulation algorithms are tailored to the characteristics of health data, allowing the incorporation of positional and spatial uncertainty into mapping and explaining geographical variation in the risk of late-stage diagnosis for breast cancer. In another study, uncertainty about cervix cancer mortality rates is propagated through the detection of significant changes in mortality across county boundaries. Both applications use a novel simulation-based multiple testing correction procedure that is very flexible and less conservative than the traditional false discovery rate approach.

Keywords: cancer; boundary detection; logistic regression; false; positives; geocoding errors.

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