Abstract

This application is for continuation of a previously funded (three year) project to develop Smoothing Spline ANOVA (SS-ANOVA) methods for risk factor detection and modeling in large complex data sets. The SS-ANOVA method attempts to model the relationship between an outcome and predictor variables using models that are more flexible than the usual parametric models. For example, the logistic model assumes that the logit is a linear combination or other simple parametric function of the predictors. The SS-ANOVA version assumes only that the logit is a 'smooth' (multivariate) function of the predictors. This smooth function is specified as the minimizer of a penalized log likelihood, defined with the aid of smoothing parameters that are chosen adaptively. A particular data set (the Wisconsin Epidemiologic Study of Diabetic Retinopathy -- WESDR) will be used to develop and test methodology.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
5R01EY009946-07
Application #
2838341
Study Section
Special Emphasis Panel (ZRG7-SSS-1 (23))
Project Start
1992-12-01
Project End
1999-11-30
Budget Start
1998-12-01
Budget End
1999-11-30
Support Year
7
Fiscal Year
1999
Total Cost
Indirect Cost

  Institution

Name
University of Wisconsin Madison
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
161202122
City
Madison
State
WI
Country
United States
Zip Code
53715

  Publications

Geng, Zhigeng; Wang, Sijian; Yu, Menggang et al. (2015) Group variable selection via convex log-exp-sum penalty with application to a breast cancer survivor study. Biometrics 71:53-62
Kong, Jing; Klein, Barbara E K; Klein, Ronald et al. (2015) Backward multiple imputation estimation of the conditional lifetime expectancy function with application to censored human longevity data. Proc Natl Acad Sci U S A 112:12069-74
Kong, Jing; Wang, Sijian; Wahba, Grace (2015) Using distance covariance for improved variable selection with application to learning genetic risk models. Stat Med 34:1708-20
Kong, Jing; Klein, Barbara E K; Klein, Ronald et al. (2012) Using distance correlation and SS-ANOVA to assess associations of familial relationships, lifestyle factors, diseases, and mortality. Proc Natl Acad Sci U S A 109:20352-7
Shi, Weiliang; Wahba, Grace; Irizarry, Rafael A et al. (2012) The partitioned LASSO-patternsearch algorithm with application to gene expression data. BMC Bioinformatics 13:98
Wahba, Grace (2010) Encoding Dissimilarity Data for Statistical Model Building. J Stat Plan Inference 140:3580-3596
Bravo, Héctor Corrada; Lee, Kristine E; Klein, Barbara E K et al. (2009) Examining the relative influence of familial, genetic, and environmental covariate information in flexible risk models. Proc Natl Acad Sci U S A 106:8128-33
Bravo, Héctor Corrada; Wright, Stephen; Eng, Kevin H et al. (2009) Estimating Tree-Structured Covariance Matrices via Mixed-Integer Programming. J Mach Learn Res 5:41-48
Shi, Weiliang; Wahba, Grace; Wright, Stephen et al. (2008) LASSO-Patternsearch algorithm with application to ophthalmology and genomic data. Stat Interface 1:137-153
Lu, Fan; Keles, Sunduz; Wright, Stephen J et al. (2005) Framework for kernel regularization with application to protein clustering. Proc Natl Acad Sci U S A 102:12332-7

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