The contrasting behavior of the MIX model is again apparent in Table 8 when one compares the results for the θ =1.5 scenario with the other columns. For Simu 1 and Simu 2 the sensitivity is generally below that of the BYM model and especially when the true relative risk is 1.5; single clusters with θ = 1.5 are simply not detected. In the 1% cluster case expected counts of at least 20 (10) are necessary to be over 95% certain of detecting a true relative risk of 2 (3) (Table 8). Note that the results of the last line of Table 8 should be discounted in view of the high probability of false-positive results corresponding to this scenario (Simu 3) for the D(0.05, 1.5) rule shown in Table 4. Thus, it is apparent that for the MIX model, it is hard to calibrate a good decision rule appropriate for a variety of spatial patterns of elevated risk. In Table 5 we summarize the results corresponding to the decision rule D(0.4, 1.5), which offers a reasonable compromise between keeping the rate of false positives below 7% and an acceptable detection rate of true clusters. With this rule true relative risks of 1.65 with an SF of 2 (i.e., average cluster with total expected count slightly under 40) or larger have more than a 50% chance of being detected, and true relative risks of around 2 are nearly always detected. However, this model does not detect a true relative risk as small as 1.3.