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The Effect of Diagnostic Misclassification on Spatial Statistics for Regional Data

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dc.contributor.advisor Horrocks, Julie
dc.contributor.advisor Berke, Olaf Scott, Christopher 2014-01-08T16:55:26Z 2014-01-08T16:55:26Z 2014-01 2013-12-18 2014-01-01
dc.description.abstract Spatial epidemiological studies which assume perfect health status information can be biased if imperfect diagnostic tests have been used to obtain the health status of individuals in a population. This study investigates the effect of diagnostic misclassification on the spatial statistical methods commonly used to analyze regional health status data in spatial epidemiology. The methods considered here are: Moran's I to assess clustering in the data, a Gaussian random field model to estimate prevalence and the range and sill parameters of the semivariogram, and Kulldorff's spatial scan test to identify clusters. Various scenarios of diagnostic misclassification were simulated from a West Nile virus dead-bird surveillance program, and the results were evaluated. It was found that non-differential misclassification added random noise to the spatial pattern in observed data which created bias in the statistical results. However, when regional sample sizes were doubled, the effect from misclassification bias on the spatial statistics decreased. en_US
dc.language.iso en en_US
dc.subject Diagnostic Misclassification en_US
dc.subject Clustering en_US
dc.subject Clusters en_US
dc.subject Spatial Epidemiology en_US
dc.subject Spatial Statistics en_US
dc.subject Regional Health Status Data en_US
dc.title The Effect of Diagnostic Misclassification on Spatial Statistics for Regional Data en_US
dc.type Thesis en_US Mathematics and Statistics en_US Master of Science en_US Department of Mathematics and Statistics en_US

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