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Table 2 Median posterior of the variance hyperparameter of the Gaussian field (\(\sigma^{2}\)) for the unadjusted and adjusted model, median posterior of variation explained (\(V\left( {Z\left( s \right)} \right)\)) and median posterior of grid specific relative risk based on residence at diagnosis

From: Bayesian spatial modelling of childhood cancer incidence in Switzerland using exact point data: a nationwide study during 1985–2015

 LGCPs
All cancersLeukaemiaLymphomaCNS tumours
\(\sigma^{2}\) unadjusteda (median, 95% CI)0.01 (0, 0.02)0.00 (0, 0.03)0.01 (0, 0.04)0.02 (0.01, 0.06)
\(\sigma^{2}\) adjustedb (median, 95% CI)0.01 (0, 0.03)0.00 (0, 0.01)0.00 (0, 0.03)0.02 (0, 0.06)
Variation explainedc (median; 95% CI)0.72 (0.43, 0.89)0.81 (0.58, 0.94)0.82 (0.60, 0.94)0.64 (0.31, 0.84)
RR unadjusteda (median; ranged)0.99 (0.83, 1.13)1.00 (0.96, 1.09)0.99 (0.9, 1.13)1.01 (0.82, 1.23)
RR adjustedb (median; ranged)1.02 (0.86, 1.08)1.00 (0.97, 1.04)1.00 (0.96, 1.07)1.00 (0.87, 1.25)
  1. CI credibility intervals, RR grid specific relative risk compared to Switzerland as a whole, LGCP log-Gaussian Cox process, CNS Central and Nervous System
  2. aThe unadjusted model refers to the models without any covariates
  3. bAdjusted for NO2, background radiation, years of general cancer registration, linguistic region and degree of urbanicity
  4. cVariation explained by the covariates from the fully adjusted model, defined as \(R^{2} = \frac{{V\left( {\varvec{X}\left( s \right)\varvec{\beta}} \right)}}{{V\left( {\varvec{X}\left( s \right)\varvec{\beta}} \right) + V\left( {Z\left( s \right)} \right)}}\) where \(V\left( \cdot \right)\) denotes the variance over the \(K\) spatial units, \(\varvec{\beta}\) is the vector of intercept and covariates, \(\varvec{X}\) the design matrix and \(Z\left( s \right)\) the Gaussian field. The variation here refers to the fully adjusted model
  5. dRange is defined as [min, max]