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Table 3 Results of the cluster detection sequential algorithm based on a suitable modification of the BYM model.

From: Geographical clustering of lung cancer in the province of Lecce, Italy: 1992–2001

Step (k) #(Δ -Δ*) 95% CI Bayes P D DIC
2 547 0.25 (0.12, 0.39) 591.44 31.67 623.10
3 341 0.29 (0.08, 0.50) 589.61 27.71 617.32
4 298 0.31 (0.08, 0.53) 578.76 24.27 612.03
5 249 0.18 (0.05, 0.32) 584.91 21.13 606.05
6 141 0.23 (0.03, 0.42) 581.26 19.93 601.20
7 102 0.32 (0.02, 0.62) 577.96 20.06 598.02
  1. The DIC criterion is defined as DIC = + p D , where is the posterior expected value of deviance (which is a measure of the goodness of fit of the model), while p D is a penalty term, i.e. the effective number of parameters given the complex interdependencies that are introduced into the actual number of parameters by the specification of correlated random effects for the risk distribution. Between two competing models the one that has a lower score of the DIC criterion should be preferred. Here, #(Δ - Δ*) denotes the number of elements in the current collection of candidate clusters: the initial set Δ was defined as the set of all circular neighbourhoods using 10% of total expected counts to define the maximum circle size. Only significant clusters for which > 0 were reported.