A hierarchical Bayesian approach to multiple testing in disease mapping.
We propose a Bayesian approach to multiple testing in disease mapping. This study was motivated by a real example regarding the mortality rate for lung cancer, males, in the Tuscan region (Italy). The data are relative to the period 1995-1999 for 287 municipalities. We develop a tri-level hierarchical Bayesian model to estimate for each area the posterior classification probability that is the posterior probability that the municipality belongs to the set of non-divergent areas. We show also the connections of our model with the false discovery rate approach. Posterior classification probabilities are used to explore areas at divergent risk from the reference while controlling for multiple testing. We consider both the Poisson-Gamma and the Besag, York and Mollié model to account for extra Poisson variability in our Bayesian formulation. Posterior inference on classification probabilities is highly dependent on the choice of the prior. We perform a sensitivity analysis and suggest how to rely on subject-specific information to derive informative a priori distributions. Hierarchical Bayesian models provide a sensible way to model classification probabilities in the context of disease mapping.
Catelan
Dolores
D
Department of Statistics "G.Parenti", University of Florence, Italy. catelan@ds.unifi.it
Lagazio
Corrado
C
Biggeri
Annibale
A
D001499:Bayes Theorem; D004812:Epidemiologic Methods; D006801:Humans; D008297:Male; D015233:Models, Statistical; D012306:Risk
Epidemiology. 1991 Jul;2(4):244-51
1912039
IARC Sci Publ. 1987;(82):1-406
3329634
Spat Spatiotemporal Epidemiol. 2009 Oct-Dec;1(1):31-40
22749413
J R Stat Soc Series B Stat Methodol. 2009 Nov 1;71(5):905-925
20694043
Biometrics. 1987 Sep;43(3):671-81
3663823
Geospat Health. 2010 May;4(2):219-29
20503190
Stat Med. 2007 Apr 15;26(8):1834-56
17221940
J Clin Epidemiol. 2008 Mar;61(3):232-240
18226745
Stat Med. 1992 Jun 15;11(8):983-1007
1496200
Stat Med. 2000 Sep 15-30;19(17-18):2217-41
10960849
Biom J. 2008 Oct;50(5):716-44
18932138
Environ Health Perspect. 2004 Jun;112(9):1016-25
15198922
Stat Med. 2000 Sep 15-30;19(17-18):2539-54
10960870