title: Semiparametric analysis of two-level bivariate binary data.
abstract: In medical studies, paired binary responses are often observed for each study subject over timepoints or clusters. A primary interest is to investigate how the bivariate association and marginal univariate risks are affected by repeated measurements on each subject. To achieve this we propose a very general class of semiparametric bivariate binary models. The subject-specific effects involved in the bivariate log odds ratio and the univariate logit components are assumed to follow a nonparametric Dirichlet process (DP). We propose a hybrid method to draw model-based inferences. In the framework of the proposed hybrid method, estimation of parameters is done by implementing the Monte Carlo expectation-maximization algorithm. The proposed methodology is illustrated through a study on the effectiveness of tibolone for reducing menopausal problems experienced by Indian women. A simulation study is also conducted to evaluate the efficiency of the new methodology.
authors.affiliation: Department of Statistics, University of Calcutta, 35 B.C. Road, Kolkata-700 019, India.
authors.forename: Malay
authors.initials: M
authors.lastname: Naskar
authors.forename: Kalyan
authors.initials: K
authors.lastname: Das
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D001699:Biometry; D003627:Data Interpretation, Statistical; D015914:Estrogen Replacement Therapy / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D009010:Monte Carlo Method; D009652:Norpregnenes / Q000008:administration & dosage / Q000627:therapeutic use
chemical_list: D009652:Norpregnenes; C027385:tibolone