Abstract

Boundary analysis concerns the detection and analysis of zones of abrupt change in spatial maps. Its importance in understanding scientific phenomena has been widely recognized in fields such as genetics and ecology. However, current methods are based upon rather ad-hoc deterministic algorithms. This project intends to develop formal statistical methods for carrying out boundary analysis, exploiting modern GIS tools to advance the development and interpretation of boundary analysis in spatial (cancer-related) maps. Attendant benefits of the project will include enhancements in the understanding of spatial structure associated with information displayed in cancer-related maps. Goals of this project include development of boundary analysis from an inferential perspective with evaluation of statistical modeling approaches using cancer data from the Minnesota Cancer Surveillance System (MCSS), the Iowa Women's Health Survey (IWHS), the Surveillance Epidemiology and End Results (SEER) (http://seer.cancer.gov) database of the National Cancer Institute, as well as Medicare usage and cancer hospice mortality data. Applications for environmental risk factor data from the Environmental Protection Agency (EPA) will also be carried out to draw toxin boundaries that may reveal interesting cancer-toxin relationships.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA112444-02
Application #
7216891
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Feuer, Eric J
Project Start
2006-04-01
Project End
2009-02-28
Budget Start
2007-03-01
Budget End
2008-02-29
Support Year
2
Fiscal Year
2007
Total Cost
$229,265
Indirect Cost

  Institution

Name
University of Minnesota Twin Cities
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
555917996
City
Minneapolis
State
MN
Country
United States
Zip Code
55455

  Publications

Quick, Harrison; Banerjee, Sudipto; Carlin, Bradley P (2013) MODELING TEMPORAL GRADIENTS IN REGIONALLY AGGREGATED CALIFORNIA ASTHMA HOSPITALIZATION DATA. Ann Appl Stat 7:154-176
Li, Pei; Banerjee, Sudipto; McBean, Alexander M (2011) Mining Boundary Effects in Areally Referenced Spatial Data Using the Bayesian Information Criterion. Geoinformatica 15:435-454
Finley, Andrew O; Banerjee, Sudipto; MacFarlane, David W (2011) A Hierarchical Model for Quantifying Forest Variables Over Large Heterogeneous Landscapes With Uncertain Forest Areas. J Am Stat Assoc 106:31-48
Banerjee, Sudipto; Finley, Andrew O; Waldmann, Patrik et al. (2010) Hierarchical Spatial Process Models for Multiple Traits in Large Genetic Trials. J Am Stat Assoc 105:506-521
Ma, Haijun; Carlin, Bradley P; Banerjee, Sudipto (2010) Hierarchical and joint site-edge methods for medicare hospice service region boundary analysis. Biometrics 66:355-64
Zhang, Yufen; Hodges, James S; Banerjee, Sudipto (2009) SMOOTHED ANOVA WITH SPATIAL EFFECTS AS A COMPETITOR TO MCAR IN MULTIVARIATE SPATIAL SMOOTHING. Ann Appl Stat 3:1805-1830
Zhang, Yufen; Banerjee, Sudipto; Yang, Rui et al. (2009) Bayesian modeling of exposure and airflow using two-zone models. Ann Occup Hyg 53:409-24
Finley, Andrew O; Banerjee, Sudipto; Waldmann, Patrik et al. (2009) Hierarchical spatial modeling of additive and dominance genetic variance for large spatial trial datasets. Biometrics 65:441-51
Liang, Shengde; Banerjee, Sudipto; Carlin, Bradley P (2009) Bayesian wombling for spatial point processes. Biometrics 65:1243-53
Finley, Andrew O; Banerjee, Sudipto; McRoberts, Ronald E (2009) HIERARCHICAL SPATIAL MODELS FOR PREDICTING TREE SPECIES ASSEMBLAGES ACROSS LARGE DOMAINS. Ann Appl Stat 3:1052-1079

Showing the most recent 10 out of 16 publications