|Grant Number:||5R01CA112444-03 Interpret this number|
|Primary Investigator:||Banerjee, Sudipto|
|Organization:||University Of Minnesota|
|Project Title:||Hierachial Modeling Approaches for Geographical Boundary Analysis in Cancer Studi|
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.
Mining Boundary Effects in Areally Referenced Spatial Data Using the Bayesian Information Criterion.
Authors: Li P, Banerjee S, McBean AM
Source: Geoinformatica, 2011 Jul;15(3), p. 435-454.
Hierarchical Spatial Process Models for Multiple Traits in Large Genetic Trials.
Authors: Banerjee S, Finley AO, Waldmann P, Ericsson T
Source: J Am Stat Assoc, 2010 Jun 1;105(490), p. 506-521.
HIERARCHICAL SPATIAL MODELS FOR PREDICTING TREE SPECIES ASSEMBLAGES ACROSS LARGE DOMAINS.
Authors: Finley AO, Banerjee S, McRoberts RE
Source: Ann Appl Stat, 2009 Sep 1;3(3), p. 1052-1079.
Gaussian predictive process models for large spatial data sets.
Authors: Banerjee S, Gelfand AE, Finley AO, Sang H
Source: J R Stat Soc Series B Stat Methodol, 2008 Sep 1;70(4), p. 825-848.
Hierarchical and joint site-edge methods for medicare hospice service region boundary analysis.
Authors: Ma H, Carlin BP, Banerjee S
Source: Biometrics, 2010 Jun;66(2), p. 355-64.
EPub date: 2009 Jul 23.
Bayesian modeling of exposure and airflow using two-zone models.
Authors: Zhang Y, Banerjee S, Yang R, Lungu C, Ramachandran G
Source: Ann Occup Hyg, 2009 Jun;53(4), p. 409-24.
EPub date: 2009 Apr 29.
Bayesian wombling for spatial point processes.
Authors: Liang S, Banerjee S, Carlin BP
Source: Biometrics, 2009 Dec;65(4), p. 1243-53.
SMOOTHED ANOVA WITH SPATIAL EFFECTS AS A COMPETITOR TO MCAR IN MULTIVARIATE SPATIAL SMOOTHING.
Authors: Zhang Y, Hodges JS, Banerjee S
Source: Ann Appl Stat, 2009;3(4), p. 1805-1830.
Hierarchical spatial modeling of additive and dominance genetic variance for large spatial trial datasets.
Authors: Finley AO, Banerjee S, Waldmann P, Ericsson T
Source: Biometrics, 2009 Jun;65(2), p. 441-51.
Parametric models for spatially correlated survival data for individuals with multiple cancers.
Authors: Diva U, Dey DK, Banerjee S
Source: Stat Med, 2008 May 30;27(12), p. 2127-44.
Order-free co-regionalized areal data models with application to multiple-disease mapping.
Authors: Jin X, Banerjee S, Carlin BP
Source: J R Stat Soc Series B Stat Methodol, 2007 Nov 1;69(5), p. 817-838.
Modelling spatially correlated survival data for individuals with multiple cancers.
Authors: Diva U, Banerjee S, Dey DK
Source: Stat Modelling, 2007 Jul 1;7(2), p. 191-213.
Flexible Cure Rate Modeling Under Latent Activation Schemes.
Authors: Cooner F, Banerjee S, Carlin BP, Sinha D
Source: J Am Stat Assoc, 2007 Jun 1;102(478), p. 560-572.
spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models.
Authors: Finley AO, Banerjee S, Carlin BP
Source: J Stat Softw, 2007 Apr;19(4), p. 1-24.
Bayesian Wombling: Curvilinear Gradient Assessment Under Spatial Process Models.
Authors: Banerjee S, Gelfand AE
Source: J Am Stat Assoc, 2006 Dec 1;101(476), p. 1487-1501.
Modelling geographically referenced survival data with a cure fraction.
Authors: Cooner F, Banerjee S, McBean AM
Source: Stat Methods Med Res, 2006 Aug;15(4), p. 307-24.