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

Published online by Cambridge University Press:  21 February 2012

Kim-Anh Do*
Affiliation:
Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA. kim@mdanderson.org
Joanne F. Aitken
Affiliation:
Queensland Cancer Fund Epidemiology Unit, Brisbane, Australia.
Adéle C. Green
Affiliation:
Epidemiology and Population Health Unit, Queensland Institute of Medical Research, Brisbane, Australia.
Nicholas G. Martin
Affiliation:
Epidemiology and Population Health Unit, Queensland Institute of Medical Research, Brisbane, Australia.
*
*Address for correspondence: Dr. K-A Do, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, Texas 77030, USA.

Abstract

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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.

Type
Articles
Copyright
Copyright © Cambridge University Press 2004