Skip to main content

Table 7 Performance comparison of geostatistical and Bayesian estimators: proportion of false positives.

From: How does Poisson kriging compare to the popular BYM model for mapping disease risks?

Estimators Lung cancer Cervix cancer
LOW RISK THRESHOLD (RR = 1) Average % best result Average % best result
BYM model 0.071 30 0.077 12
Point Poisson kriging (adjacent counties) 0.069 20 0.066 14
ATA Poisson kriging (adjacent counties) 0.070 30 0.063 24
ATA Poisson kriging (32 neighbors) 0.072 20 0.061 50
HIGH RISK THRESHOLD RR = 1.1   RR = 1.25  
BYM model 0.069 20 0.080 4
Point Poisson kriging (adjacent counties) 0.066 14 0.065 12
ATA Poisson kriging (adjacent counties) 0.064 36 0.064 32
ATA Poisson kriging (32 neighbors) 0.064 30 0.060 52
  1. Results obtained on average over 50 realizations generated for Regions 1 and 2. Poisson kriging was conducted using either adjacent counties (same neighbors as BYM Model) or the 32 closest counties in terms of distance between population-weighted centroids. ATA kriging accounts for the shape and size of the counties in the analysis. The proportion of false positives is derived from Receiver Operating Characteristic (ROC) curves computed using a low (Relative Risk, RR = 1) and high risk threshold (RR = 1.1, RR = 1.25). Bold numbers refer to best performances: few false positives. The second column gives the percentage of realizations where the particular method yields the best results.