Abstract

Cancer maps can provide important clues concerning geographical variability in the etiology, prevention, screelling or treatment of cancer. As in all medical research, it ts important to determine whether any variation observed may reasonably be due to chance or not. This can be done using tests for spatial randomness, adjusting for the uneven geographical population density. Many such tests have been proposed, but for most, little is known about their properties, and they are seldomly used in cancer atlases. In this methodological project we will (i) develop theoretical properties that any test for spatial randomness should fulfill in order to be useful for cancer maps, (ii) determine which test statistics do and do not fulfifi these properties, (iii) evaluate the statistical power of different test statistics for different alternative hypotheses, (iv) determine the ability of different tests to estimate cluster model parameters when the null hypothesis is rejected, and (v) evaluate and ifiustrate the practical use of different test statistics on, among other data sets, county based brain cancer mortality data from the United States and individually geocoded breast cancer treatment data from Connecticut.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
1R01CA095979-01
Application #
6479823
Study Section
Special Emphasis Panel (ZRG1-SNEM-2 (02))
Program Officer
Tiwari, Ram C
Project Start
2002-08-01
Project End
2006-07-31
Budget Start
2002-08-01
Budget End
2003-07-31
Support Year
1
Fiscal Year
2002
Total Cost
$367,706
Indirect Cost

  Institution

Name
University of Connecticut
Department
Public Health & Prev Medicine
Type
Schools of Medicine
DUNS #
City
Farmington
State
CT
Country
United States
Zip Code
06030

  Publications

Abrams, Allyson M; Kleinman, Ken; Kulldorff, Martin (2010) Gumbel based p-value approximations for spatial scan statistics. Int J Health Geogr 9:61
Puett, Rc; Lawson, Ab; Clark, Ab et al. (2010) Power Evaluation of Focused Cluster Tests. Environ Ecol Stat 17:303-316
Hinrichsen, Virginia L; Klassen, Ann C; Song, Changhong et al. (2009) Evaluation of the performance of tests for spatial randomness on prostate cancer data. Int J Health Geogr 8:41
Song, Changhong; Kulldorff, Martin (2006) Likelihood based tests for spatial randomness. Stat Med 25:825-39
Kulldorff, Martin; Song, Changhong; Gregorio, David et al. (2006) Cancer map patterns: are they random or not? Am J Prev Med 30:S37-49
Rusiecki, Jennifer A; Kulldorff, Martin; Nuckols, John R et al. (2006) Geographically based investigation of prostate cancer mortality in four U.S. Northern Plain states. Am J Prev Med 30:S101-8
Fang, Zixing; Kulldorff, Martin; Gregorio, David I (2004) Brain cancer mortality in the United States, 1986 to 1995: a geographic analysis. Neuro Oncol 6:179-87