论文标题
使用潜在高斯工艺的贝叶斯生存树分区模型
A Bayesian Survival Tree Partition Model Using Latent Gaussian Processes
论文作者
论文摘要
生存模型用于分析各种学科的事件时间数据。比例危害模型提供了可解释的参数估计，但是比例危害假设并不总是合适的。非参数模型更灵活，但通常缺乏明确的推论框架。我们提出了一个既灵活又推论的贝叶斯树分区模型。推理是通过后树结构获得的，并通过使用潜在的凸起的高斯工艺对每个分区中的危险函数进行建模来保留柔韧性。有效的可逆跳跃马尔可夫链蒙特卡洛算法是通过通过拉普拉斯近似在每个分区元素中边缘化的参数来完成的。建立了估算器的一致性属性。该方法可用于帮助确定子组中的亚组以及预后和/或预测性生物标志物。该方法应用于肝存活数据集，并将其与一些现有方法进行比较。
Survival models are used to analyze time-to-event data in a variety of disciplines. Proportional hazard models provide interpretable parameter estimates, but proportional hazards assumptions are not always appropriate. Non-parametric models are more flexible but often lack a clear inferential framework. We propose a Bayesian tree partition model which is both flexible and inferential. Inference is obtained through the posterior tree structure and flexibility is preserved by modeling the the hazard function in each partition using a latent exponentiated Gaussian process. An efficient reversible jump Markov chain Monte Carlo algorithm is accomplished by marginalizing the parameters in each partition element via a Laplace approximation. Consistency properties for the estimator are established. The method can be used to help determine subgroups as well as prognostic and/or predictive biomarkers in time-to-event data. The method is applied to a liver survival dataset and is compared with some existing methods on simulated data.
