基于分段确定性蒙特卡洛抽样的多重危害模型平均化——用于长期存活数据

多重危害模型是一类用于建模长期生存的灵活参数模型。其可加危害结构允许灵活、非比例危害，其特征可随时间变化，同时保持参数形式，从而实现对研究观察期之外的生存外推。然而，需大量用户输入，才能选择要建模的潜在危害数量、其分布以及将哪些变量关联到每个危害。 resulting set of models is too large to explore manually, limiting their practical usefulness. Motivated by applications to stroke survivor and kidney transplant patient survival times we extend the standard polyhazard model through a prior structure allowing for joint inference of parameters and structural quantities, and develop a sampling scheme that utilises state-of-the-art Piecewise Deterministic Markov Processes to sample from the resulting transdimensional posterior with minimal user tuning.