Analysis of melanoma onset: Assessing familial aggregation by using estimating equations and fitting variance components via Bayesian random effects models
Analysis of melanoma onset: Assessing familial aggregation by using estimating equations and fitting variance components via Bayesian random effects models
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DOI：
10.1375/twin.7.1.98
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发表时间：
2004-02-01
期刊：
影响因子：
--
通讯作者：
Martin, NG
中科院分区：
文献类型：
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作者：
Do, KA;Aitken, JF;Martin, NG
我们调查遗传和共享环境因素的相对贡献是否与黑色素瘤风险增加相关。对来自昆士兰州家族性黑色素瘤项目的数据进行了分析，该项目包括来自1912个家庭的15，907名受试者，以估计黑色素瘤发病年龄变化的加性遗传、常见和独特环境贡献。两种互补的方法来分析相关的发病时间的家庭数据被认为是：广义估计方程（GEE）的方法，其中一个可以估计的关系特定的依赖，同时回归系数，描述了平均人口的反应，不断变化的协变量;和一个主题-特定的贝叶斯混合模型，其中回归参数的异质性被明确建模，并且可以直接估计。使用比例风险和Weibull模型，因为两者都产生了估计相对风险的自然框架，同时调整了其他协变量的同时效应。使用简单的马尔可夫链蒙特卡罗方法对缺失数据进行协变量插补，贝叶斯模型的实际实施基于使用免费软件包BUGS的Gibbs抽样。此外，我们还使用贝叶斯模型来研究遗传和环境影响对痣和雀斑（黑色素瘤的已知危险因素）表达的相对贡献。
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.