Evaluation of the performance of tests for spatial randomness on prostate cancer data

DSpace/Manakin Repository

Evaluation of the performance of tests for spatial randomness on prostate cancer data

Citable link to this page

 

 
Title: Evaluation of the performance of tests for spatial randomness on prostate cancer data
Author: Hinrichsen, Virginia L; Klassen, Ann C.; Song, Changhong; Kulldorff, Martin

Note: Order does not necessarily reflect citation order of authors.

Citation: Hinrichsen, Virginia L., Ann C. Klassen, Changhong Song, and Martin Kulldorff. 2009. Evaluation of the performance of tests for spatial randomness on prostate cancer data. International Journal of Health Geographics 8: 41.
Full Text & Related Files:
Abstract: Background: Spatial global clustering tests can be used to evaluate the geographical distribution of health outcomes. The power of several of these tests has been evaluated and compared using simulated data, but their performance using real unadjusted data and data adjusted for individual- and area-level covariates has not been reported previously. We evaluated data on prostate cancer histologic tumor grade and stage of disease at diagnosis for incident cases of prostate cancer reported to the Maryland Cancer Registry during 1992–1997. We analyzed unadjusted data as well as expected counts from models that were adjusted for individual- level covariates (race, age and year of diagnosis) and area-level covariates (census block group median household income and a county-level socioeconomic index). We chose 3 spatial clustering tests that are commonly used to evaluate the geographic distribution of disease: Cuzick-Edwards' k-NN (k-Nearest Neighbors) test, Moran's I and Tango's MEET (Maximized Excess Events Test). Results: For both grade and stage at diagnosis, we found that Cuzick-Edwards' k-NN and Moran's I were very sensitive to the percent of population parameter selected. For stage at diagnosis, all three tests showed that the models with individual- and area-level adjustments reduced clustering the most, but did not reduce it entirely. Conclusion: Based on this specific example, results suggest that these tests provide useful tools for evaluating spatial clustering of disease characteristics, both before and after consideration of covariates.
Published Version: doi:10.1186/1476-072X-8-41
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4878939
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)

 
 

Search DASH


Advanced Search
 
 

Submitters