Flexible Bayesian P-splines for smoothing age-specific spatio-temporal mortality patterns

被引:10
作者
Goicoa, T. [1 ,2 ,3 ]
Adin, A. [1 ,2 ]
Etxeberria, J. [1 ,2 ,4 ]
Militino, A. F. [1 ,2 ]
Ugarte, M. D. [1 ,2 ]
机构
[1] Univ Publ Navarra, Dept Stat & Operat Res, Campus Arrosadia, Pamplona 31006, Spain
[2] Univ Publ Navarra, Inst Adv Mat InaMat, Pamplona, Spain
[3] Res Network Hlth Serv Chron Dis REDISSEC, Zaragoza, Spain
[4] Consortium Biomed Res Epidemiol & Publ Hlth CIBER, Madrid, Spain
关键词
Breast cancer mortality; disease mapping; integrated nested Laplace approximations; smoothing; time-space-age models; BREAST-CANCER MORTALITY; TIME TRENDS; B-SPLINES; MODELS; SPAIN; INFERENCE;
D O I
10.1177/0962280217726802
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
In this paper age-space-time models based on one and two-dimensional P-splines with B-spline bases are proposed for smoothing mortality rates, where both fixed relative scale and scale invariant two-dimensional penalties are examined. Model fitting and inference are carried out using integrated nested Laplace approximations, a recent Bayesian technique that speeds up computations compared to McMC methods. The models will be illustrated with Spanish breast cancer mortality data during the period 1985-2010, where a general decline in breast cancer mortality has been observed in Spanish provinces in the last decades. The results reveal that mortality rates for the oldest age groups do not decrease in all provinces.
引用
收藏
页码:384 / 403
页数:20
相关论文
共 49 条
[1]   Breast cancer mortality in Spain: Has it really declined for all age groups? [J].
Alvaro-Meca, A. ;
Debon, A. ;
Gil Prieto, R. ;
Gil de Miguel, A. .
PUBLIC HEALTH, 2012, 126 (10) :891-895
[2]  
[Anonymous], 2010, Implementing approximate bayesian inference using integrated nested laplace approximation: A manual for the inla program
[3]  
[Anonymous], R LANG ENV STAT COMP
[4]  
[Anonymous], 2000, STAT MODELS EPIDEMIO
[5]   Cancer screening in Spain [J].
Ascunce, N. ;
Salas, D. ;
Zubizarreta, R. ;
Almazan, R. ;
Ibanez, J. ;
Ederra, M. .
ANNALS OF ONCOLOGY, 2010, 21 :iii43-iii51
[6]   Bayesian penalized spline models for the analysis of spatio-temporal count data [J].
Bauer, Cici ;
Wakefield, Jon ;
Rue, Havard ;
Self, Steve ;
Feng, Zijian ;
Wang, Yu .
STATISTICS IN MEDICINE, 2016, 35 (11) :1848-1865
[7]   Breast cancer incidence and mortality trends in 16 European countries [J].
Botha, JL ;
Bray, F ;
Sankila, R ;
Parkin, DM .
EUROPEAN JOURNAL OF CANCER, 2003, 39 (12) :1718-1729
[8]   APPROXIMATE INFERENCE IN GENERALIZED LINEAR MIXED MODELS [J].
BRESLOW, NE ;
CLAYTON, DG .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1993, 88 (421) :9-25
[9]   Comparing INLA and OpenBUGS for hierarchical Poisson modeling in disease mapping [J].
Carroll, R. ;
Lawson, A. B. ;
Faes, C. ;
Kirby, R. S. ;
Aregay, M. ;
Watjou, K. .
SPATIAL AND SPATIO-TEMPORAL EPIDEMIOLOGY, 2015, 14-15 :45-54
[10]   Time trends of breast cancer mortality in Spain during the period 1977-2001 and Bayesian approach for projections during 2002-2016 [J].
Cleries, R. ;
Ribes, J. ;
Esteban, L. ;
Martinez, J. M. ;
Borras, J. M. .
ANNALS OF ONCOLOGY, 2006, 17 (12) :1783-1791