Childhood Cancer Cluster Simulation - Cancer in Brief Manuscript Schündeln et al. 2020

Incidence of newly diagnosed childhood cancer (140/1,000,000 children under 15 years) and nephroblastoma (7/1,000,000) was simulated. Clusters of defined size (1 to 50) were randomly assembled on the district level in Germany. Each cluster was simulated with different relative risk levels (1 to 100). For each combination 2000 iterations were done. Simulated data was then analyzed by three local clustering tests: Besag-Newell method, spatial scan statistic and Bayesian Besag-York-Mollié with Integrated Nested Laplace Approximation approach. The operating characteristics of all three methods were systematically documented (sensitivity, specificity, positive/negative predictive values, exact and minimum power, correct classification, positive/negative diagnostic likelihood and false positive/negative rate). The performance of each of the various cluster detection methods and scenarios in this study is reported according to the quality criteria detailed below. Minimum Power (MP): Proportion of simulations detecting at least one district of the true cluster. Exact Power (EP): Proportion of simulations detecting the true cluster without false positives. Sensitivity (sens): Proportion of correctly detected districts in the true cluster. Specificity (spec): Percentage of normal risk districts, correctly classified as normal risk districts. Positive predictive value (PPV): Proportion of districts in the detected cluster belonging to the true cluster. Negative predictive value (NPV): Proportion of districts not labeled as a risk cluster that is not part of the true cluster. Correct classification (CC): Percentage of correctly classified districts of all districts. Correct proportion (CP): Correctly labeled districts of all detected potential high-risk districts. Positive diagnostic likelihood (PDL): The ratio of high-risk districts being detected, divided by the probability non-high-risk districts being detected (sensitivity / (1-specificity). Negative diagnostic likelihood (NDL): The ratio of high-risk districts not being detected divided by the probability of non-high-risk districts not being detected ((1 – sensitivity) /specificity). False positive rate (FPR): Incorrectly labeled high-risk districts of all detected high-risk districts False negative rate (FNR): Incorrectly labeled normal-risk districts of all detected normal-risk district