|Grant Number:||5R01CA101901-03 Interpret this number|
|Primary Investigator:||Qaqish, Bahjat|
|Organization:||Univ Of North Carolina Chapel Hill|
|Project Title:||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.
ORTH: R and SAS software for regression models of correlated binary data based on orthogonalized residuals and alternating logistic regressions.
Authors: By K. , Qaqish B.F. , Preisser J.S. , Perin J. , Zink R.C. .
Source: Computer methods and programs in biomedicine, 2014 Feb; 113(2), p. 557-68.
EPub date: 2013-10-31.
Orthogonalized residuals for estimation of marginally specified association parameters in multivariate binary data.
Authors: Qaqish B.F. , Zink R.C. , Preisser J.S. .
Source: Scandinavian journal of statistics, theory and applications, 2012-09-01; 39(3), p. 515-527.
EPub date: 2012-7-02.
Deletion diagnostics for alternating logistic regressions.
Authors: Preisser J.S. , By K. , Perin J. , Qaqish B.F. .
Source: Biometrical journal. Biometrische Zeitschrift, 2012 Sep; 54(5), p. 701-15.
EPub date: 2012-07-06.