Document Detail

Semiparametric analysis of two-level bivariate binary data.
MedLine Citation:
PMID:  17156274     Owner:  NLM     Status:  MEDLINE    
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.
Malay Naskar; Kalyan Das
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Biometrics     Volume:  62     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  2006 Dec 
Date Detail:
Created Date:  2006-12-12     Completed Date:  2007-03-07     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1004-13     Citation Subset:  IM    
Department of Statistics, University of Calcutta, 35 B.C. Road, Kolkata-700 019, India.
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MeSH Terms
Analysis of Variance
Data Interpretation, Statistical
Estrogen Replacement Therapy / statistics & numerical data
Likelihood Functions
Models, Statistical
Monte Carlo Method
Norpregnenes / administration & dosage,  therapeutic use
Reg. No./Substance:
0/Norpregnenes; 5630-53-5/tibolone

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