Weibull Racing Survival Analysis with Competing Events, Left Truncation, and Time-varying Covariates
Weibull Racing Survival Analysis with Competing Events, Left Truncation, and Time-varying Covariates
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发表时间：
2022
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通讯作者：
Quan Zhang;Mingyuan Zhou
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作者：
Quan Zhang;Mingyuan Zhou
我们提出了贝叶斯非参数威布尔代表竞赛（WDR）的生存分析与竞争事件，并实现模型的可解释性和灵活性。利用生存竞争事件的自然机制，我们假设一个潜在的无限数量的子事件之间的比赛。在此过程中，WDR适应非线性协变量效应，无需数据转换。此外，WDR能够处理左截断、时变协变量、不同类型的删失以及缺失事件时间或类型。我们开发了一个有效的MCMC算法的基础上吉布斯抽样贝叶斯推理，并提供了一个R包。合成数据分析和基准方法的比较表明，WDR的出色性能和简约的非线性建模能力。此外，我们分析了两个真实的数据集，并展示了WDR的优势。具体来说，我们1 ar X iv：1911。01 82 7v 3 [ st at .M E ] 2 2 M ar 2 02 2研究三种类型淋巴瘤的死亡时间，并显示WDR在建模非线性协变量效应和发现新疾病方面的潜力。我们还使用WDR来调查轻度认知障碍的发病年龄，并解释生物标志物对阿尔茨海默病进展的加速或减速作用。
We propose Bayesian nonparametric Weibull delegate racing (WDR) for survival analysis with competing events and achieve both model interpretability and flexibility. Utilizing a natural mechanism of surviving competing events, we assume a race among a potentially infinite number of sub-events. In doing this, WDR accommodates nonlinear covariate effects with no need of data transformation. Moreover, WDR is able to handle left truncation, time-varying covariates, different types of censoring, and missing event times or types. We develop an efficient MCMC algorithm based on Gibbs sampling for Bayesian inference and provide an R package. Synthetic data analysis and comparison with benchmark approaches demonstrate WDR’s outstanding performance and parsimonious nonlinear modeling capacity. In addition, we analyze two real data sets and showcase advantages of WDR. Specifically, we 1 ar X iv :1 91 1. 01 82 7v 3 [ st at .M E ] 2 2 M ar 2 02 2 study time to death of three types of lymphoma and show the potential of WDR in modeling nonlinear covariate effects and discovering new diseases. We also use WDR to investigate the age at onset of mild cognitive impairment and interpret the accelerating or decelerating effects of biomarkers on the progression of Alzheimer’s disease.