Genetic analysis of the age at menopause by using estimating equations and Bayesian random effects models

Do, K. A., Broom, B. M., Kuhnert, P., Duffy, D. L., Todorov, A. A., Treloar, S. A. and Martin, N. G. (2000) Genetic analysis of the age at menopause by using estimating equations and Bayesian random effects models. Statistics in Medicine, 19 9: 1217-1235. doi:10.1002/(SICI)1097-0258(20000515)19:9<1217::AID-SIM421>3.3.CO;2-H


Author Do, K. A.
Broom, B. M.
Kuhnert, P.
Duffy, D. L.
Todorov, A. A.
Treloar, S. A.
Martin, N. G.
Title Genetic analysis of the age at menopause by using estimating equations and Bayesian random effects models
Journal name Statistics in Medicine   Check publisher's open access policy
ISSN 0277-6715
Publication date 2000-05-15
Sub-type Article (original research)
DOI 10.1002/(SICI)1097-0258(20000515)19:9<1217::AID-SIM421>3.3.CO;2-H
Volume 19
Issue 9
Start page 1217
End page 1235
Total pages 19
Place of publication Sussex, U.K.
Publisher John Wiley
Language eng
Subject C1
321011 Medical Genetics
730116 Reproductive system and disorders
Abstract Multi-wave self-report data on age at menopause in 2182 female twin pairs (1355 monozygotic and 827 dizygotic pairs), were analysed to estimate the genetic, common and unique environmental contribution to variation in age at menopause. Two complementary approaches for analysing correlated time-to-onset twin data are considered: the generalized estimating equations (GEE) method in which one can estimate zygosity-specific dependence simultaneously with regression coefficients that describe the average population response to changing covariates; and a subject-specific Bayesian mixed model in which heterogeneity in regression parameters is explicitly modelled and the different components of variation may be estimated directly. The proportional hazards and Weibull models were utilized, as both produce natural frameworks for estimating relative risks while adjusting for simultaneous effects of other covariates. A simple Markov chain Monte Carlo method for covariate imputation of missing data was used and the actual implementation of the Bayesian model was based on Gibbs sampling using the freeware package BUGS. Copyright (C) 2000 John Wiley & Sons, Ltd.
Keyword Statistics & Probability
Public, Environmental & Occupational Health
Medical Informatics
Medicine, Research & Experimental
Correlated Binary Regression
Large Complex Pedigrees
Monte-carlo Estimation
Linkage Analysis
Combined Segregation
Natural Menopause
Disease Incidence
Life-tables
Of-onset
Survival
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Medicine Publications
School of Psychology Publications
Centre for Military and Veterans' Health Publications
 
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Created: Tue, 10 Jun 2008, 21:27:56 EST