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A dependent Dirichlet process model for survival data with competing risks
Lifetime Data Analysis ( IF 1.2 ) Pub Date : 2020-10-12 , DOI: 10.1007/s10985-020-09506-0
Yushu Shi 1 , Purushottam Laud 2 , Joan Neuner 2
Affiliation  

In this paper, we first propose a dependent Dirichlet process (DDP) model using a mixture of Weibull models with each mixture component resembling a Cox model for survival data. We then build a Dirichlet process mixture model for competing risks data without regression covariates. Next we extend this model to a DDP model for competing risks regression data by using a multiplicative covariate effect on subdistribution hazards in the mixture components. Though built on proportional hazards (or subdistribution hazards) models, the proposed nonparametric Bayesian regression models do not require the assumption of constant hazard (or subdistribution hazard) ratio. An external time-dependent covariate is also considered in the survival model. After describing the model, we discuss how both cause-specific and subdistribution hazard ratios can be estimated from the same nonparametric Bayesian model for competing risks regression. For use with the regression models proposed, we introduce an omnibus prior that is suitable when little external information is available about covariate effects. Finally we compare the models’ performance with existing methods through simulations. We also illustrate the proposed competing risks regression model with data from a breast cancer study. An R package “DPWeibull” implementing all of the proposed methods is available at CRAN.



中文翻译:

具有竞争风险的生存数据的依赖狄利克雷过程模型

在本文中,我们首先提出了一个依赖狄利克雷过程 (DDP) 模型,该模型使用威布尔模型的混合,每个混合分量类似于生存数据的 Cox 模型。然后,我们为没有回归协变量的竞争风险数据构建 Dirichlet 过程混合模型。接下来,我们通过对混合成分中的子分布风险使用乘法协变量效应,将此模型扩展到竞争风险回归数据的 DDP 模型。尽管建立在比例风险(或子分布风险)模型之上,但提议的非参数贝叶斯回归模型不需要假设风险(或子分布风险)比率恒定。生存模型中还考虑了外部时间相关协变量。描述完模型后,我们讨论了如何从竞争风险回归的相同非参数贝叶斯模型中估计特定原因和子分布风险比。为了与所提出的回归模型一起使用,我们引入了一个综合先验,适用于关于协变量效应的外部信息很少的情况。最后,我们通过模拟将模型的性能与现有方法进行比较。我们还使用来自乳腺癌研究的数据说明了提议的竞争风险回归模型。CRAN 提供了实现所有建议方法的 R 包“DPWeibull”。最后,我们通过模拟将模型的性能与现有方法进行比较。我们还使用来自乳腺癌研究的数据说明了提议的竞争风险回归模型。CRAN 提供了实现所有建议方法的 R 包“DPWeibull”。最后,我们通过模拟将模型的性能与现有方法进行比较。我们还使用来自乳腺癌研究的数据说明了提议的竞争风险回归模型。CRAN 提供了实现所有建议方法的 R 包“DPWeibull”。

更新日期:2020-10-12
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