论文标题
贝叶斯非参数生存分析的预测方法
A Predictive Approach to Bayesian Nonparametric Survival Analysis
论文作者
论文摘要
贝叶斯非参数方法是分析生存数据的流行选择，因为它们能够灵活地对生存时间的分布进行模拟。这些方法通常在生存函数上采用非参数先验，该生存函数相对于右审核数据是共轭的。引起这些先验，尤其是在协变量存在的情况下，可能具有挑战性，推断通常依赖于计算密集的马尔可夫链蒙特卡洛方案。在本文中，我们基于最新的工作，该工作重铸贝叶斯推断是在观察到的样本上有条件的人口的看不见值分配的预测分布，从而避免了需要指定复杂的先验。我们描述了一个基于Copula的预测更新，该更新接受了可扩展的顺序重要性采样算法，以执行适当解释右审查的推论。我们通过扩展DOOB的一致性定理提供理论上的理由，并在许多模拟和真实数据集（包括与协变量的示例）上说明了方法。我们的方法使分析师仅通过预测分布的规范执行贝叶斯非参数推断。
Bayesian nonparametric methods are a popular choice for analysing survival data due to their ability to flexibly model the distribution of survival times. These methods typically employ a nonparametric prior on the survival function that is conjugate with respect to right-censored data. Eliciting these priors, particularly in the presence of covariates, can be challenging and inference typically relies on computationally intensive Markov chain Monte Carlo schemes. In this paper, we build on recent work that recasts Bayesian inference as assigning a predictive distribution on the unseen values of a population conditional on the observed samples, thus avoiding the need to specify a complex prior. We describe a copula-based predictive update which admits a scalable sequential importance sampling algorithm to perform inference that properly accounts for right-censoring. We provide theoretical justification through an extension of Doob's consistency theorem and illustrate the method on a number of simulated and real data sets, including an example with covariates. Our approach enables analysts to perform Bayesian nonparametric inference through only the specification of a predictive distribution.
