Analysis of Melanoma Onset: Assessing Familial Aggregation by Using Estimating Equations and Fitting Variance Components via Bayesian Random Effects Models

Do, Kim-Anh, Aitken, Joanne F., Green, Adele C and Martin, Nicholas G. (2004) Analysis of Melanoma Onset: Assessing Familial Aggregation by Using Estimating Equations and Fitting Variance Components via Bayesian Random Effects Models. Twin Research, 7 1: 98-113.


Author Do, Kim-Anh
Aitken, Joanne F.
Green, Adele C
Martin, Nicholas G.
Title Analysis of Melanoma Onset: Assessing Familial Aggregation by Using Estimating Equations and Fitting Variance Components via Bayesian Random Effects Models
Journal name Twin Research  (ERA 2012 Listed)    (ERA 2010 Rank B)   Check publisher's open access policy
Publication date 2004
Sub-type Article
DOI 10.1375/13690520460741480
Volume number 7
Issue number 1
ISSN 1369-0523
Start page 98
End page 113
Total pages 16
Editor N. G. Martin
K. M. Kirk
Place of publication Bowen Hills, Australia
Publisher Australian Academic Press
Collection year 2004
Language eng
Subject C1
321011 Medical Genetics
730108 Cancer and related disorders
Abstract We investigate whether relative contributions of genetic and shared environmental factors are associated with an increased risk in melanoma. Data from the Queensland Familial Melanoma Project comprising 15,907 subjects arising from 1912 families were analyzed to estimate the additive genetic, common and unique environmental contributions to variation in the age at onset of melanoma. Two complementary approaches for analyzing correlated time-to-onset family data were considered: the generalized estimating equations (GEE) method in which one can estimate relationship-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 modeled 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 free ware package BUGS. In addition, we also used a Bayesian model to investigate the relative contribution of genetic and environmental effects on the expression of naevi and freckles, which are known risk factors for melanoma.
Keyword Genetics & Heredity
Obstetrics & Gynecology
Reproductive Biology
Large Complex Pedigrees
Censored Survival-data
Monte-carlo Estimation
Age-of-onset
Linkage Analysis
Semiparametric Estimation
Combined Segregation
Mixed Models
Life-tables
Major Gene
Q-Index Code C1

 
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