Polyhazard models are a class of flexible parametric models for modelling survival over extended time horizons. Their additive hazard structure allows for flexible, non-proportional hazards whose characteristics can change over time while retaining a parametric form, which allows for survival to be extrapolated beyond the observation period of a study. Significant user input is required, however, in selecting the number of latent hazards to model, their distributions and the choice of which variables to associate with each hazard. The 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.

翻译：多风险模型是一类灵活的参数化模型，适用于长时间跨度的生存建模。其加性风险结构允许灵活的非比例风险，其特性可随时间变化，同时保持参数化形式，这使得生存预测能够外推至研究观察期之外。然而，模型构建需要大量用户干预，包括选择潜在风险的数量、其分布形式以及确定各风险关联的变量。由此产生的模型集合过于庞大，难以通过人工方式探索，这限制了其实用价值。基于对中风幸存者和肾移植患者生存时间的应用需求，我们通过引入先验结构扩展了标准多风险模型，实现了参数与结构量的联合推断，并开发了一种采样方案。该方案利用先进的分段确定性马尔可夫过程，能够以最小的用户调参需求，从由此产生的跨维度后验分布中进行采样。