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Profile likelihood approaches for semiparametric copula and frailty models for clustered survival data

机译:针对聚类生存数据的半参数copula和脆弱模型的轮廓似然方法

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In clustered survival data, the dependence among individual survival times within a cluster has usually been described using copula models and frailty models. In this paper we propose a profile likelihood approach for semiparametric copula models with different cluster sizes. We also propose a likelihood ratio method based on profile likelihood for testing the absence of association parameter (i.e. test of independence) under the copula models, leading to the boundary problem of the parameter space. For this purpose, we show via simulation study that the proposed likelihood ratio method using an asymptotic chi-square mixture distribution performs well as sample size increases. We compare the behaviors of the two models using the profile likelihood approach under a semiparametric setting. The proposed method is demonstrated using two well-known data sets.
机译:在聚类生存数据中,通常使用copula模型和脆弱模型描述了聚类中个体生存时间之间的依赖性。在本文中,我们针对具有不同聚类大小的半参数copula模型提出了一种轮廓似然法。我们还提出了一种基于轮廓似然性的似然比方法,用于测试在copula模型下是否存在关联参数(即独立性测试),从而导致参数空间的边界问题。为此,我们通过仿真研究表明,使用渐近卡方混合物分布的拟议似然比方法在样本量增加时效果很好。我们使用半参数设置下的轮廓似然方法比较两个模型的行为。使用两个众所周知的数据集演示了该方法。

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