Estimation of correlation and association between multivariate binary outcomes is of interest in various settings including family studies, community studies, analysis of social networks and analysis of medical practice data. This project deals with three aspects of such models; the parameter space, estimation and regression diagnostics. First, we propose a theoretical study of the parameter space to develop an understanding of issues of existence and uniqueness of marginal models. This is pivotal to any estimation, computation and simulation of such models. Second, we have recently developed a new method based on orthogonalized residuals for constructing estimating equations for estimation of association parameters such as odds ratios, moment correlations and kappas. In this project we propose to refine and evaluate this approach in large samples via efficiency calculations and in small samples via simulation studies. We also propose to compare this approach to existing methods based on estimating equations, alternating logistic regressions and pseudo-likelihoods. Special emphasis is given to moderate and variable cluster sizes, a case where the performance of existing methods needs further investigation. The third major aim is the development of methodology and computational tools for regression diagnostics including leverage and influence in the context of regression models for association parameters. This will be carried out in the framework provided by the estimating equations based on orthogonalized residuals. Overall, this project will develop new understanding, knowledge, methods and tools for modeling association in multivariate binary outcomes, and eventually lead to better analysis of such data from medical, public health and social studies. ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA101901-03
Application #
7173256
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Feuer, Eric J
Project Start
2005-03-01
Project End
2009-02-28
Budget Start
2007-03-01
Budget End
2009-02-28
Support Year
3
Fiscal Year
2007
Total Cost
$175,054
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
By, Kunthel; Qaqish, Bahjat F; Preisser, John S et al. (2014) ORTH: R and SAS software for regression models of correlated binary data based on orthogonalized residuals and alternating logistic regressions. Comput Methods Programs Biomed 113:557-68
Preisser, John S; By, Kunthel; Perin, Jamie et al. (2012) Deletion diagnostics for alternating logistic regressions. Biom J 54:701-15
Qaqish, Bahjat F; Zink, Richard C; Preisser, John S (2012) Orthogonalized residuals for estimation of marginally specified association parameters in multivariate binary data. Scand Stat Theory Appl 39:515-527