论文标题
当前状态数据的贝叶斯非参数二元生存回归
Bayesian Nonparametric Bivariate Survival Regression for Current Status Data
论文作者
论文摘要
我们考虑基于当前状态数据的事件时间分布的非参数推断。我们表明，在这种情况下，传统的混合先验，包括流行的Dirichlet工艺混合物，导致生物学上无法解释的结果，因为它们不自然地将事件时间的概率质量偏向观察到的数据的极端。对依赖审查的简单假设可以解决问题。然后，我们将讨论扩展到双变量当前状态数据，并部分排序两个结果。除了依赖审查外，我们还利用了有关两个事件时间的一些最小的已知结构。我们为后验模拟设计了马尔可夫链蒙特卡洛算法。该方法应用于复发性感染研究，提供了有关与症状相关的医院就诊如何影响协变量的新见解。
We consider nonparametric inference for event time distributions based on current status data. We show that in this scenario conventional mixture priors, including the popular Dirichlet process mixture prior, lead to biologically uninterpretable results as they unnaturally skew the probability mass for the event times toward the extremes of the observed data. Simple assumptions on dependent censoring can fix the problem. We then extend the discussion to bivariate current status data with partial ordering of the two outcomes. In addition to dependent censoring, we also exploit some minimal known structure relating the two event times. We design a Markov chain Monte Carlo algorithm for posterior simulation. Applied to a recurrent infection study, the method provides novel insights into how symptoms-related hospital visits are affected by covariates.
