Type of Document	Dissertation
Author	Lin, Lanjia
Author's Email Address	lanjia@stat.fsu.edu
URN	etd-04112009-143028
Title	Association Models For Clustered Data With Binary And Continuous Responses
Degree	Doctor of Philosophy
Department	Statistics, Department of
Advisory Committee	Advisor Name Title Debajyoti Sinha Committee Chair Daniel McGee Committee Member Stuart R. Lipsitz Committee Member Myra Hurt Outside Committee Member
Keywords	Dirichlet Process Prior Bivariate Binary And Continuous Responses Copula Model Bridge Distribution Bayesian Analysis MCMC
Date of Defense	2009-04-08
Availability	unrestricted
Abstract This dissertation develops novel single random effect models as well as bivariate correlated random effects model for clustered data with bivariate mixed responses. Logit and identity link functions are used for the binary and continuous responses. For the ease of interpretation of the regression effects, random effect of the binary response has bridge distribution so that the marginal model of mean of the binary response after integrating out the random effect preserves logistic form. And the marginal regression function of the continuous response preserves linear form. Within-cluster and within-subject associations could be measured by our proposed models. For the bivariate correlated random effects model, we illustrate how different levels of the association between two random effects induce different Kendall’s tau values for association between the binary and continuous responses from the same cluster. Fully parametric and semi-parametric Bayesian methods as well as maximum likelihood method are illustrated for model analysis. In the semiparametric Bayesian model, normality assumption of the regression error for the continuous response is relaxed by using a nonparametric Dirichlet Process prior. Robustness of the bivariate correlated random effects model using ML method to misspecifications of regression function as well as random effect distribution is investigated by simulation studies. The Bayesian and likelihood methods are applied to a developmental toxicity study of ethylene glycol in mice.
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