Smoothing and high risk areas detection in space-time disease mapping: a comparison of P-splines,autoregressive, and moving average models |
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| Authors: | A?Adin M?A?Martínez-Beneito P?Botella-Rocamora T?Goicoa Email author" target="_blank">M?D?UgarteEmail author |
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| Institution: | 1.Department of Statistics and O.R.,Public University of Navarre,Pamplona,Spain;2.Institute for Advanced Materials (InaMat),Public University of Navarre,Pamplona,Spain;3.Foundation for the Promotion of Health and Biomedical Research of Valencian Region (FISABIO),Valencia,Spain;4.CIBER of Epidemiology an Public Health CIBERESP,Madrid,Spain;5.Research Network on Health Services in Chronic Diseases (REDISSEC),Madrid,Spain |
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| Abstract: | Recently, several models have been proposed for smoothing risks in disease mapping. These models consider different ways of introducing both spatial and temporal dependence as well as spatio-temporal interactions. In this work, a comparison among some autoregressive, moving average, and P-spline models is performed. Firstly, brain cancer mortality data are used to analyze the degree of smoothness introduced by these models. Secondly, two separate simulation studies (one model-based and the other model-free) are carried out to evaluate the model performance in terms of bias, variability, sensitivity, and specificity. We conclude that P-spline models seem to be a good alternative to autoregressive and moving average models when analyzing highly sparse disease mapping data. |
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