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

K. A. Do, B. M. Broom, P. Kuhnert, D. L. Duffy, A. A. Todorov, S. A. Treloar, N. G. Martin

Research output: Contribution to journalArticle

44 Scopus citations

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. (C) 2000 John Wiley and Sons, Ltd.

Original languageEnglish
Pages (from-to)1217-1235
Number of pages19
JournalStatistics in medicine
Volume19
Issue number9
DOIs
StatePublished - May 15 2000

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