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

• Published in: Statistics in medicine Vol. 19; no. 9; pp. 1217 - 1235
• Main Authors: Do, K-A., Broom, B. M., Kuhnert, P., Duffy, D. L., Todorov, A. A., Treloar, S. A., Martin, N. G.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 15.05.2000; Wiley
• Subjects: Adult; Age Factors; Aged; Aged, 80 and over; Alcohol Drinking; Australia; Bayes Theorem; Biological and medical sciences; Body Mass Index; Educational Status; Female; Fundamental and applied biological sciences. Psychology; Hormone metabolism and regulation; Humans; Mammalian female genital system; Markov Chains; Menarche; Menopause - genetics; Menopause - physiology; Middle Aged; Models, Genetic; Monte Carlo Method; Parity; Smoking; Social Class; Surveys and Questionnaires; Vertebrates: reproduction; Australia; Markov chain; Bayes estimation; Human; Monte Carlo method; Statistical model; Menopause; Random effect; Female; Gibbs sampling; Genetic determinism; Age
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/(SICI)1097-0258(20000515)19:9<1217::AID-SIM421>3.0.CO;2-Q

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 © 2000 John Wiley & Sons, Ltd.