Sophisticated computer programs known as geographic information systems (GISs) have revolutionized the analysis of spatially referenced datasets, through their ability to ``layer''multiple data sources over a common study area. However, methods for statistical inference on these complex datasets are only now beginning to develop. In this proposal we develop statistical methodology in five specific aim areas related to cancer control and epidemiology. First, we develop hierarchical predictive process approaches to easing the problems associated with the need to repeatedly invert large matrices in fitting geostatistical models to large datasets. Second, we propose new methods for handling multivariate marked point processes, as would be required for a spatial point pattern where the points are marked by the type of cancer or perhaps treatment selection of each individual. Third, we consider semiparametric hierarchical models for cancer survival data using mixtures of Polya trees. Fourth, we consider the analysis of continuous-time spatiotemporal data arising from longitudinal experiments designed to estimate functional relationships. Fifth, describe a suite of R packages that help integrate necessary georeferenced database and display components with hierarchical modeling capability, thus bringing the hierarchical spatial analysis we propose to a far broader potential audience than is currently possible. We provide several cancer-related examples to illustrate the methods we propose.

Public Health Relevance

The relevance of this work to public health lies in its ability to improve the understanding and decision-making abilities of state-based professionals engaged in planning for comprehensive cancer control programs. Our focus is squarely on real problems in cancer research, including determining whether women with breast cancer who live further from radiation treatment facilities are significantly more likely to opt for mastectomy over breast conserving surgery (``lumpectomy""""""""), and investigating the change in estimated UV exposure levels over time by geographic region, and whether these levels are associated with higher rates of skin cancer.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA095955-06
Application #
7673804
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Dunn, Michelle C
Project Start
2002-04-01
Project End
2011-07-31
Budget Start
2009-08-01
Budget End
2010-07-31
Support Year
6
Fiscal Year
2009
Total Cost
$341,124
Indirect Cost
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
Carlin, Bradley P; Zhong, Wei; Koopmeiners, Joseph S (2013) Discussion of 'small-sample behavior of novel phase I cancer trial designs' by Assaf P Oron and Peter D Hoff. Clin Trials 10:81-5; discussion 88-92
Renfro, Lindsay A; Carlin, Bradley P; Sargent, Daniel J (2012) Bayesian adaptive trial design for a newly validated surrogate endpoint. Biometrics 68:258-67
Hobbs, Brian P; Sargent, Daniel J; Carlin, Bradley P (2012) Commensurate Priors for Incorporating Historical Information in Clinical Trials Using General and Generalized Linear Models. Bayesian Anal 7:639-674
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Hanson, Timothy E; Monteiro, João V D; Jara, Alejandro (2011) The Polya Tree Sampler: Towards Efficient and Automatic Independent Metropolis-Hastings Proposals. J Comput Graph Stat 20:41-62
Hatfield, Laura A; Gutreuter, Steve; Boogaard, Michael A et al. (2011) Multilevel empirical bayes modeling for improved estimation of toxicant formulations to suppress parasitic sea lamprey in the upper great lakes. Biometrics 67:1153-62
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
Hanson, Timothy E; Jara, Alejandro; Zhao, Luping (2011) A Bayesian Semiparametric Temporally-Stratified Proportional Hazards Model with Spatial Frailties. Bayesian Anal 6:1-48
MacLehose, Richard F; Oakes, J Michael; Carlin, Bradley P (2011) Turning the Bayesian crank. Epidemiology 22:365-7
Gu, Yu; Sinha, Debajyoti; Banerjee, Sudipto (2011) Analysis of cure rate survival data under proportional odds model. Lifetime Data Anal 17:123-34

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