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Detecting prevalent clusters of Multimorbidity with uncertainty evaluation using Bayesian Mixture Modelling and subsampling

   School of Mathematics and Statistics

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  Dr Michail Papathomas, Dr Nicolò Margaritella  Applications accepted all year round  Competition Funded PhD Project (Students Worldwide)

About the Project

Multimorbidity is the presence of at least two diseases in an individual. MM is one of the major challenges faced by health systems across the world. In the UK, the care of multimorbid patients accounts for more than half of the primary and secondary care costs, with an expectation that the cost will rise. This project will utilise the Ninewells Hospital & Medical School dataset that follows the Scottish Tayside and Fife population. We will first identify MM patients, focusing on those with complex MM (more than 4 conditions). We will then utilise Bayesian Mixture Modelling to detect the prevalent clusters of MM (Papathomas et al. 2012, Jing and Papathomas 2021), with patients in the same cluster characterised by conditions of a homogenous range and nature. The use of Bayesian modelling will allow for the evaluation of uncertainty with regard to the number of clusters and also the typical subject profile within each one of the MM clusters. Profile Regression (Liverani et al. 2015), a popular extension of Bayesian Mixture Modelling, will be used to model the effectiveness of different treatments for these patients. As this concerns a large dataset, subsampling techniques will be used, which will lead to further developments in the field of Bayesian Mixture Modelling when handling large datasets.

For more information, please see the School's Postgraduate Research page, and in particular the information about Statistics PhD opportunities.

Funding Notes

Full funding (fees, plus stipend of approx. £15,840) is available for well-qualified students; we encourage applications as soon as possible to maximize your chances of being funded. UK, EU and other overseas students are all encouraged to apply. New PhD students would typically start in September 2022, but this is flexible. More information is available School's Postgraduate Research web page -- please see the link at the bottom of the project description.


Jing, W. and Papathomas M. (2021) Challenges and proposals for Dirichlet process mixture models with Gaussian kernels. In preparation.
Liverani, S., Hastie, D. I., Azizi, L., Papathomas, M. and Richardson, S. (2015) PReMiuM: An R package for Profile Regression Mixture Models using Dirichlet Processes. Journal of Statistical Software. 64, Issue 7, pp 1-30.
Papathomas, M., Molitor, J., Hoggart, C., Hastie, D. and Richardson, S. (2012) Exploring data from genetic association studies using Bayesian variable selection and the Dirichlet process: application to searching for gene-gene patterns. Genetic Epidemiology. 36, 663-67

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