Smoothing and high risk areas detection in space-time disease mapping: a comparison of P-splines, autoregressive, and moving average models

被引：23

作者：

Adin, A. ^{[1
,2
]}

Martinez-Beneito, M. A. ^{[3
,4
]}

Botella-Rocamora, P. ^{[3
]}

Goicoa, T. ^{[1
,2
,5
]}

Ugarte, M. D. ^{[1
,2
]}

机构：

[1] Univ Publ Navarra, Dept Stat & OR, Pamplona, Spain

[2] Univ Publ Navarra, Inst Adv Mat InaMat, Pamplona, Spain

[3] Fdn Promot Hlth & Biomed Res Valencian Reg FISABI, Valencia, Spain

[4] CIBER Epidemiol Publ Hlth CIBERESP, Madrid, Spain

[5] Res Network Hlth Serv Chron Dis REDISSEC, Madrid, Spain

来源：

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT | 2017年 / 31卷 / 02期

关键词：

Autoregressive models; Moving average models; P-spline models; Risk smoothing; Risk variability; Sensitivity; Specificity; APPROXIMATE INFERENCE; EMPIRICAL BAYES;

D O I：

10.1007/s00477-016-1269-8

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页码：403 / 415

页数：13