Selecting factors predictive of heterogeneity in multivariate event time data.
Selecting factors predictive of heterogeneity in multivariate event time data.
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选择预测多变量事件时间数据异质性的因素。
DOI：
10.1111/j.0006-341x.2004.00179.x
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发表时间：
2004
期刊：
影响因子：
1.9
通讯作者：
Chen,Zhen
中科院分区：
文献类型：
--
作者：
Dunson,DavidB;Chen,Zhen
In multivariate survival analysis, investigators are often interested in testing for heterogeneity among clusters, both overall and within specific classes. We represent different hypotheses about the heterogeneity structure using a sequence of gamma frailty models, ranging from a null model with no random effects to a full model having random effects for each class. Following a Bayesian approach, we define prior distributions for the frailty variances consisting of mixtures of point masses at zero and inverse-gamma densities. Since frailties with zero variance effectively drop out of the model, this prior allocates probability to each model in the sequence, including the overall null hypothesis of homogeneity. Using a counting process formulation, the conditional posterior distributions of the frailties and proportional hazards regression coefficients have simple forms. Posterior computation proceeds via a data augmentation Gibbs sampling algorithm, a single run of which can be used to obtain model-averaged estimates of the population parameters and posterior model probabilities for testing hypotheses about the heterogeneity structure. The methods are illustrated using data from a lung cancer trial.
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