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@ARTICLE{Schndeln:166613,
      author       = {M. M. Schündeln and T. Lange and M. Knoll$^*$ and C. Spix
                      and H. Brenner$^*$ and K. Bozorgmehr and C. Stock$^*$},
      title        = {{S}tatistical methods for spatial cluster detection in
                      childhood cancer incidence: {A} simulation study.},
      journal      = {Cancer epidemiology},
      volume       = {70},
      issn         = {1877-7821},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2020-03049},
      pages        = {101873},
      year         = {2021},
      note         = {#LA:C070#Volume 70, February 2021, 101873},
      abstract     = {The potential existence of spatial clusters in childhood
                      cancer incidence is a debated topic. Identification of such
                      clusters may help to better understand etiology and develop
                      preventive strategies. We evaluated widely used statistical
                      approaches to cluster detection in this context.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 (1-50) were randomly
                      assembled on the district level in Germany. Each cluster was
                      simulated with different relative risk levels (1-100). 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
                      (sensitivity, specificity, predictive values, power and
                      correct classification) of all three methods were
                      systematically described.Performance varied considerably
                      within and between methods, depending on the simulated
                      setting. Sensitivity of all methods was positively
                      associated with increasing size, incidence and RR of the
                      high-risk area. Besag-York-Mollié showed highest
                      specificity for minimally increased RR in most scenarios.
                      The performance of all methods was lower in the
                      nephroblastoma scenario compared with the scenario including
                      all cancer cases.This study illustrates the challenge to
                      make reliable inferences on the existence of spatial
                      clusters based on single statistical approaches in childhood
                      cancer. Application of multiple methods, ideally with known
                      operating characteristics, and a critical discussion of the
                      joint evidence seems recommendable when aiming to identify
                      high-risk clusters.},
      keywords     = {Bayesian (Other) / Besag York Mollié (Other) /
                      Besag-Newell (Other) / Childhood cancer (Other) / Spatial
                      cluster (Other) / Spatial scan statistic (Other)},
      cin          = {E050 / C070 / C120 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)E050-20160331 / I:(DE-He78)C070-20160331 /
                      I:(DE-He78)C120-20160331 / I:(DE-He78)HD01-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33360605},
      doi          = {10.1016/j.canep.2020.101873},
      url          = {https://inrepo02.dkfz.de/record/166613},
}