The standard approach to modeling survival times, or more generally, time to event data, is often based on parametric assumptions that may not fit the data collected well. One of the goals of this article is to discuss and compare several commonly used parametric and non-parametric, as well as a Bayesian semi-parametric method for survival data. We do so in the context of the data from an experimental system where insect herbivores become infected when consuming a lethal virus along with the plant on which the virus resides. We used data collected on individual insects that were fed known doses of virus along with varying genotypes of a single plant species (soybean), to compare how the insect’s diet affects its time to death. Through hazard characterization and model selection, we found that the flexible semi-parametric analysis is better at describing the time-to-death data while maintaining a relatively parsimonious form. Unlike the standard parametric and non-parametric approaches, the Bayesian semi-parametric approach better captured the rapid decline in the hazard function after a window of time where the host was most vulnerable to the virus. For our study system, being able to accurately model time to death and quantify how plant genetics affects within-insect disease processes allows us to gain a better understanding of the host-pathogen interaction at an individual level. While we show the appropriateness of the Bayesian semi-parametric approach for infection data, this method readily applies to data sets concerned with characterizing a time until any event.

中文翻译：

---

基于贝叶斯的生存分析：推断宿主-病原体相互作用中的死亡时间

建模生存时间或更普遍的事件发生时间的标准方法通常是基于可能无法很好收集数据的参数假设。本文的目的之一是讨论和比较几种常用的参数化和非参数化以及贝叶斯半参数化生存数据方法。我们是根据来自实验系统的数据来进行的，在该系统中，食用致命病毒以及该病毒所在的植物会感染食草动物。我们使用收集的昆虫个体数据收集已知剂量的病毒，以及单一植物物种（大豆）的不同基因型，以比较昆虫的饮食如何影响其死亡时间。通过危害特征描述和模型选择，我们发现，灵活的半参数分析更适合描述死亡时间数据，同时保持相对简约的形式。与标准的参数方法和非参数方法不同，贝叶斯半参数方法可以更好地捕获宿主在最容易受到病毒感染的一段时间后危险功能的迅速下降。对于我们的研究系统，能够准确地模拟死亡时间并量化植物遗传学如何影响昆虫内部疾病进程，使我们能够更好地了解个体水平上的宿主-病原体相互作用。尽管我们展示了贝叶斯半参数方法对感染数据的适用性，但该方法很容易应用于与表征任何事件之前的时间有关的数据集。