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Table 3 Performance comparison of alternative estimators: mean square error of prediction. 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

4.426

4.462

4.777

1178

1461

1321

Population-weighted average

3.249

1.085

8.159

8.093

3.753

11.40

Global Empirical Bayes

5.090

4.591

2.286

17.09

9.536

6.853

Local Empirical Bayes

1.514

0.811

2.424

8.631

5.543

11.02

Poisson kriging (true γR(h))

0.828

0.593

2.376

6.154

3.534

9.648

Poisson kriging

0.857

0.625

2.452

7.107

3.741

10.03

CERVIX CANCER

      

Observed rates

0.803

0.802

0.738

367

257

232

Population-weighted average

0.590

0.149

1.006

1.088

0.617

1.468

Global Empirical Bayes

0.556

0.496

0.318

1.387

1.079

0.906

Local Empirical Bayes

0.264

0.116

0.341

3.143

1.685

3.440

Poisson kriging (true γR(h))

0.177

0.041

0.313

0.881

0.566

1.233

Poisson kriging

0.179

0.045

0.335

1.117

0.764

1.383