This research is for the further development of a new class of multi variate semi-parametric model building methods' known collectively as Smoothing Spline Analysis of Variance, (SS-ANOVA) which are suitable for the analysis of data from large cohort studies, either epidemiologic or clinical trials, with many qualitatively different variables observed over several time points. These methods represent an attempt to obtain flexible empirical relationships between multiple complex responses and predictors. If such models can be fitted to the data, then estimated sensitivities of the responses to various predictors can be obtained and the existence of associations between various variables of interest can be tested. The models reduce to standard parametric models if the data suggest that nonparametric terms in the model are not present. SS-ANOVA models have been built and tested for the prediction of multi variate and multi categorical responses and methods developed which allow the analysis of large complex data sets. The investigators will extend this work in several directions: Development of methods to prescreen large, complex data sets for patterns of relationships that warrant further examination; more sophisticated model selection methods, extension to nonparametric multi variate density estimation for the purpose of uncovering conditional and time dependent relationships among the variables, and the development of threshold models. Data from the Wisconsin Epidemiological Study of Diabetic Retinopathy and the Beaver Dam Eye Study will be used to examine the models under study for their reasonableness and for their ability to answer questions meaningful to the study scientists. The results will have broad applicability to other large epidemiological studies as well as to clinical trials. The research software will be developed into a user friendly form, documented, and made publicly available.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
2R01EY009946-08
Application #
6043542
Study Section
Special Emphasis Panel (ZRG1-SNEM-5 (01))
Program Officer
Everett, Donald F
Project Start
1992-12-01
Project End
2002-12-31
Budget Start
2000-01-01
Budget End
2000-12-31
Support Year
8
Fiscal Year
2000
Total Cost
$160,461
Indirect Cost
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
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
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. (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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