Semiparametric Analysis of Two‐Level Bivariate Binary Data

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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Biometrics Jg. 62; H. 4; S. 1004 - 1013
Hauptverfasser:	Naskar, Malay, Das, Kalyan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Malden, USA Blackwell Publishing Inc 01.12.2006 International Biometric Society Blackwell Publishing Ltd
Schlagworte:	Algorithms Analysis of Variance Approximation Binary data Biometrics Biometry Bivariate binary Data analysis Data Interpretation, Statistical Datasets Dirichlet process Estimation methods Estrogen Replacement Therapy - statistics & numerical data Female Humans Inference Likelihood Functions MCEM menopause Modeling Models, Statistical Monte Carlo Method Monte Carlo simulation Multivariate analysis Norpregnenes - administration & dosage Norpregnenes - therapeutic use odds ratio Parametric models risk Semiparametric model Standard error women
ISSN:	0006-341X, 1541-0420
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	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.
Bibliographie:	http://dx.doi.org/10.1111/j.1541-0420.2006.00618.x ark:/67375/WNG-S2KWH0Z1-Z ArticleID:BIOM618 istex:94E5899B9598F2E581332F06B1D3D3BEE920023A SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ISSN:	0006-341X 1541-0420
DOI:	10.1111/j.1541-0420.2006.00618.x