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Cancer map patterns: are they random or not?

Martin Kulldorff1, Changhong Song, David Gregorio

  • 1Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, Boston, Massachusetts 02215, USA. Martin_Kulldorff@hms.harvard.edu

American Journal of Preventive Medicine
|February 7, 2006
PubMed
Summary
This summary is machine-generated.

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Evaluating spatial patterns in cancer maps is crucial. Tango's MEET and the spatial scan statistic are recommended for assessing geographic variations in cancer data, distinguishing true patterns from random chance.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Geographic Information Systems (GIS) in Public Health

Background:

  • Cancer maps visualize geographic variations in incidence, mortality, and treatment.
  • Distinguishing true geographic patterns from random fluctuations is essential for accurate interpretation.

Purpose of the Study:

  • To evaluate the performance of nine spatial randomness tests on various cancer datasets.
  • To identify reliable statistical methods for analyzing geographic patterns in cancer maps.

Main Methods:

  • Applied nine spatial randomness tests to diverse cancer data (incidence, mortality, treatment, stage).
  • Utilized data at different spatial resolutions for breast, prostate, thyroid, and nasopharynx cancers.
  • Tested methods on datasets from Connecticut and the U.S.

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Main Results:

  • Tango's MEET, Oden's Ipop, and spatial scan statistic demonstrated strong performance across datasets.
  • Besag-Newell's R, Cuzick-Edwards k-NN, and Turnbull's CEPP performance varied with parameter choice.
  • Moran's I generally performed poorly; Swartz Entropy and Whittemore's tests showed inconsistent results.

Conclusions:

  • Recommends Tango's MEET (global clustering) and spatial scan statistic (cluster detection) for analyzing cancer maps.
  • These tests effectively evaluate spatial patterns, aiding in cancer control and hypothesis generation.