||5R01CA101901-03 Interpret this number
||Univ Of North Carolina Chapel Hill
||Estimation of Association for Multivariate Binary Data
DESCRIPTION (provided by applicant): 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.
Orthogonalized residuals for estimation of marginally specified association parameters in multivariate binary data.
Qaqish BF, Zink RC, Preisser JS
Scand Stat Theory Appl, 2012 Sep 1;39(3), p. 515-527.
2012 Jul 2.
Deletion diagnostics for alternating logistic regressions.
Preisser JS, By K, Perin J, Qaqish BF
Biom J, 2012 Sep;54(5), p. 701-15.
2012 Jul 6.