Literature DB >> 20414368

ANALYSIS OF MINNESOTA COLON AND RECTUM CANCER POINT PATTERNS WITH SPATIAL AND NONSPATIAL COVARIATE INFORMATION.

Shengde Liang1, Bradley P Carlin, Alan E Gelfand.   

Abstract

Colon and rectum cancer share many risk factors, and are often tabulated together as "colorectal cancer" in published summaries. However, recent work indicating that exercise, diet, and family history may have differential impacts on the two cancers encourages analyzing them separately, so that corresponding public health interventions can be more efficiently targeted. We analyze colon and rectum cancer data from the Minnesota Cancer Surveillance System from 1998-2002 over the 16-county Twin Cities (Minneapolis-St. Paul) metro and exurban area. The data consist of two marked point patterns, meaning that any statistical model must account for randomness in the observed locations, and expected positive association between the two cancer patterns. Our model extends marked spatial point pattern analysis in the context of a log Guassian Cox process to accommodate spatially referenced covariates (local poverty rate and location within the metro area), individual-level risk factors (patient age and cancer stage), and related interactions. We obtain smoothed maps of marginal log-relative intensity surfaces for colon and rectum cancer, and uncover significant age and stage differences between the two groups. This encourages more aggressive colon cancer screening in the inner Twin Cities and their southern and western exurbs, where our model indicates higher colon cancer relative intensity.

Entities:  

Year:  2008        PMID: 20414368      PMCID: PMC2857924          DOI: 10.1214/09-AOAS240

Source DB:  PubMed          Journal:  Ann Appl Stat        ISSN: 1932-6157            Impact factor:   2.083


  14 in total

1.  ANALYSIS OF MINNESOTA COLON AND RECTUM CANCER POINT PATTERNS WITH SPATIAL AND NONSPATIAL COVARIATE INFORMATION.

Authors:  Shengde Liang; Bradley P Carlin; Alan E Gelfand
Journal:  Ann Appl Stat       Date:  2008-10-08       Impact factor: 2.083

2.  An estimating function approach to inference for inhomogeneous Neyman-Scott processes.

Authors:  Rasmus Plenge Waagepetersen
Journal:  Biometrics       Date:  2007-03       Impact factor: 2.571

3.  Bayesian Wombling: Curvilinear Gradient Assessment Under Spatial Process Models.

Authors:  Sudipto Banerjee; Alan E Gelfand
Journal:  J Am Stat Assoc       Date:  2006-12-01       Impact factor: 5.033

4.  Magnesium intake and reduced risk of colon cancer in a prospective study of women.

Authors:  Aaron R Folsom; Ching-Ping Hong
Journal:  Am J Epidemiol       Date:  2005-11-30       Impact factor: 4.897

5.  Comparison of microsatellite instability, CpG island methylation phenotype, BRAF and KRAS status in serrated polyps and traditional adenomas indicates separate pathways to distinct colorectal carcinoma end points.

Authors:  Michael J O'Brien; Shi Yang; Charline Mack; Huihong Xu; Christopher S Huang; Elizabeth Mulcahy; Mark Amorosino; Francis A Farraye
Journal:  Am J Surg Pathol       Date:  2006-12       Impact factor: 6.394

6.  Distinguishing right from left colon by the pattern of gene expression.

Authors:  Oleg K Glebov; Luz M Rodriguez; Kenneth Nakahara; Jean Jenkins; Janet Cliatt; Casey-Jo Humbyrd; John DeNobile; Peter Soballe; Richard Simon; George Wright; Patrick Lynch; Sherri Patterson; Henry Lynch; Steven Gallinger; Aby Buchbinder; Gary Gordon; Ernest Hawk; Ilan R Kirsch
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2003-08       Impact factor: 4.254

7.  Relations between amount and type of alcohol and colon and rectal cancer in a Danish population based cohort study.

Authors:  A Pedersen; C Johansen; M Grønbaek
Journal:  Gut       Date:  2003-06       Impact factor: 23.059

8.  Gaussian predictive process models for large spatial data sets.

Authors:  Sudipto Banerjee; Alan E Gelfand; Andrew O Finley; Huiyan Sang
Journal:  J R Stat Soc Series B Stat Methodol       Date:  2008-09-01       Impact factor: 4.488

9.  Comparison of risk factors for colon and rectal cancer.

Authors:  Esther K Wei; Edward Giovannucci; Kana Wu; Bernard Rosner; Charles S Fuchs; Walter C Willett; Graham A Colditz
Journal:  Int J Cancer       Date:  2004-01-20       Impact factor: 7.396

10.  Bayesian wombling for spatial point processes.

Authors:  Shengde Liang; Sudipto Banerjee; Bradley P Carlin
Journal:  Biometrics       Date:  2009-12       Impact factor: 2.571

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  5 in total

1.  ANALYSIS OF MINNESOTA COLON AND RECTUM CANCER POINT PATTERNS WITH SPATIAL AND NONSPATIAL COVARIATE INFORMATION.

Authors:  Shengde Liang; Bradley P Carlin; Alan E Gelfand
Journal:  Ann Appl Stat       Date:  2008-10-08       Impact factor: 2.083

2.  Bayesian log-Gaussian Cox process regression: with applications to meta-analysis of neuroimaging working memory studies.

Authors:  Pantelis Samartsidis; Claudia R Eickhoff; Simon B Eickhoff; Tor D Wager; Lisa Feldman Barrett; Shir Atzil; Timothy D Johnson; Thomas E Nichols
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2018-06-29       Impact factor: 1.864

3.  A space-time point process model for analyzing and predicting case patterns of diarrheal disease in northwestern Ecuador.

Authors:  Jaeil Ahn; Timothy D Johnson; Darlene Bhavnani; Joseph N S Eisenberg; Bhramar Mukherjee
Journal:  Spat Spatiotemporal Epidemiol       Date:  2014-03-13

4.  Modeling Bronchiolitis Incidence Proportions in the Presence of Spatio-Temporal Uncertainty.

Authors:  Matthew J Heaton; Candace Berrett; Sierra Pugh; Amber Evans; Chantel Sloan
Journal:  J Am Stat Assoc       Date:  2019-05-31       Impact factor: 5.033

5.  Impact of socioeconomic inequalities on geographic disparities in cancer incidence: comparison of methods for spatial disease mapping.

Authors:  Juste Aristide Goungounga; Jean Gaudart; Marc Colonna; Roch Giorgi
Journal:  BMC Med Res Methodol       Date:  2016-10-12       Impact factor: 4.615

  5 in total

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