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Table 1 Summary results of 1000 realizations from an inhomogeneous Poisson process

From: Detecting cancer clusters in a regional population with local cluster tests and Bayesian smoothing methods: a simulation study

Lung cancer cases Cluster Expected1(mean) Observed (mean) RR = 2.02 SIR (mean) RR = 2.0 SIR 95% Poisson CI3RR = 2.0 Observed (mean) RR = 4.02 SIR (mean) RR = 4.0 SIR 95% Poisson CI3RR = 4.0
Census tracts level         
Males Urban cluster 18 35 1.92 1.38-2.67 69 3.78 3.03-4.85
Females Urban cluster 6 13 2.13 1.26-3.72 25 4.24 2.27-7.04
Males Rural cluster 3 6 2.08 0.90-4.45 12 4.11 2.82-6.17
Females Rural cluster 1 2 2.2 0.50-7.99 4 4.32 1.5-10.66
Males No cluster, urban 1000 996 1 0.94-1.06 998 1 0.94-1.10
No cluster, rural 1004 0.94-1.06 1010 1 0.95-1.07
Females No cluster, urban 358 369 1 0.94-1.06 358 1 0.9-1.1
No cluster, rural 371 1.03 0.94-1.15 363 1 0.91-1.12
Community level         
Males Urban cluster 49 66 1.35 1.10-1.71 99 2.03 1.66-2.46
Females Urban cluster 18 25 1.36 0.94-2.10 37 2.07 1.49-2.84
Males Rural cluster 30 33 1.11 0.78-1.55 39 1.32 0.95-1.78
Females Rural cluster 11 12 1.06 0.62-1.92 13 1.25 0.69-2.04
Males No cluster, urban 942 934 1 0.93-1.06 966 1.03 0.99-1.13
No cluster, rural 967 1.03 0.96-1.09 983 1.04 0.98-1.11
Females No cluster, urban 336 333 1 0.9-1.1 346 1.03 0.93-1.14
  No cluster, rural   346 1.03 0.93-1.1 354 1.05 0.95-1.17
  1. CI = confidence interval of 1000 realizations.
  2. 1Expected under the null hypothesis (= background incidence).
  3. 2Observed with sampling using an inhomogeneous Poisson process.
  4. 3Boice-Monson Method.