Bayesian inference in survival analysis: new approaches to modelling and computation

生存模型在数据科学的许多应用领域中无处不在，特别是在临床试验产生的数据建模方面，但也越来越多地来自观察性研究，其中的数据更容易获得，但通常也更结构化和异构。为了准确地从观测数据中学习，必须采取更细致和复杂的建模策略，这反过来又使稳健和可靠的推理成为一项挑战。该项目的第一个目标是开发符合目的的生存模型，以便与卫生经济评估中使用的现代数据集一起使用。在此之后，我们将为这些模型开发健壮且可扩展的推理算法，借鉴贝叶斯计算中使用的最先进方法的思想并以此为基础。该项目将与众多工业伙伴进行协商和合作，例如ICON Plc的研究人员，这是一家与主要制药公司和政府机构合作的全球咨询公司，以确保开发的任何方法都符合目的并符合监管框架。为了完成该项目，将开发定制软件包，为稳健的贝叶斯生存模型提供端到端的数据科学管道。研究方向：统计学与应用概率运筹学

Survival models are ubiquitous in many application areas of data science, in particular in modelling data arising from clinical trials but also increasingly from observational studies, in which data are more freely available but typically also more structured and heterogeneous. To accurately learn from observational data more nuanced and complex modelling strategies must be taken, which in turn makes robust and reliable inference a challenge. The first aim of this project are to develop fit-for-purpose survival models for use with modern datasets used in health economic evaluation. Following this, we will develop robust and scalable inference algorithms for these models, borrowing ideas from and building upon state-of-the-art approaches used in Bayesian computation. The project will be done in consultation and collaboration with numerous industrial partners, such as researchers at ICON Plc, a global consultancy company working with major pharmaceutical companies and government bodies, to ensure that any methods developed are both fit-for-purpose and fall within regulatory frameworks. To complete the project bespoke software packages will be developed to provide an end-to-end data science pipeline for robust Bayesian survival modelling.Research Areas: Statistics and applied probability Operational Research