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Table 4 Performance comparison of alternative estimators: rank correlation coefficient between estimates and true risk values. Results obtained on average over 100 realizations generated under two different population size scenarios and 3 types of risk map (1 = observed, 2 = smooth, 3 = random). Poisson kriging was conducted with the semivariogram estimated from the underlying risk values (true γR(h)) or the simulated mortality rates. Bold numbers refer to best performances outside the ideal case where the true semivariogram of risk is known.

From: Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging

Estimators WF population BF population
BREAST CANCER Scenario 1 Scenario 2 Scenario 3 Scenario 1 Scenario 2 Scenario 3
Observed rates 0.813 0.761 0.805 0.355 0.328 0.199
Population-weighted average 0.792 0.899 0.047 0.623 0.737 -0.001
Global Empirical Bayes 0.751 0.720 0.834 0.129 0.146 0.363
Local Empirical Bayes 0.896 0.922 0.825 0.612 0.708 0.217
Poisson kriging (true γR(h)) 0.930 0.927 0.822 0.699 0.744 0.257
Poisson kriging 0.929 0.925 0.818 0.654 0.732 0.225
CERVIX CANCER       
Observed rates 0.765 0.682 0.770 -0.191 -0.282 0.101
Population-weighted average 0.706 0.886 0.006 0.445 0.650 -0.017
Global Empirical Bayes 0.748 0.664 0.811 0.314 0.225 0.354
Local Empirical Bayes 0.867 0.906 0.789 0.479 0.598 0.189
Poisson kriging (true γR(h)) 0.905 0.967 0.807 0.593 0.676 0.228
Poisson kriging 0.903 0.961 0.790 0.558 0.611 0.200