title: A Bayesian hierarchical model of nontraumatic lower-extremity amputation rates.
abstract: A Bayesian hierarchical generalized linear model is used to estimate the risk of lower-extremity amputations (LEA) among diabetes patients from different counties in the state of Missouri. The model includes fixed age effects, fixed gender effect, random geographic effects, and spatial correlations between neighboring counties. The computation is done by Gibbs sampling using OPENBUGS. DIC (Deviance Information Criterion) is used as a criterion of goodness of fit to examine age effects, gender effect, and spatial correlations among counties in the risks of having LEAs. The Bayesian estimates are also shown to be quite robust in terms of choices of hyper-parameters.
authors: University of Missouri-Columbia, Columbia, MO, USA.
authors: Xiaoyi
authors: X
authors: Min
authors: Dongchu
authors: D
authors: Sun
authors: Zhuoqiong
authors: Z
authors: He
authors: Mario
authors: M
authors: Schootman
mesh_terms: D000671:Amputation, Surgical / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D048909:Diabetes Complications / Q000453:epidemiology / Q000601:surgery; D017719:Diabetic Foot / Q000453:epidemiology* / Q000601:surgery*; D005260:Female; D006801:Humans; D015994:Incidence; D016014:Linear Models; D016015:Logistic Models; D035002:Lower Extremity / Q000601:surgery; D008297:Male; D008923:Missouri / Q000453:epidemiology; D015233:Models, Statistical; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D017678:Sex Distribution; D062206:Spatial Analysis; D019371:Topography, Medical