[School of Natural Sciences PhD Scholarships] Joint Modelling of Longitudinal and Time-to-Event Data for Extreme Patient Outcomes

About the project

About the Project This PhD project will develop new statistical methods for jointly modelling longitudinal measurements and time-to-event outcomes in patients exhibiting extreme or unusual measurement patterns. Longitudinal data, collected repeatedly over time, can provide valuable information about how a patient’s condition changes, while time-to-event data capture important outcomes such as disease progression, hospitalisation, or death. However, existing joint modelling approaches may perform poorly when longitudinal measurements contain extreme values, exhibit heavy-tailed behaviour, or follow unusual trajectories. The project will develop flexible Bayesian joint models that can account for extreme and heavy-tailed longitudinal behaviour while appropriately modelling the associated time-to-event process. The research will draw on longitudinal data analysis, survival analysis, joint modelling and extreme value modelling, with methodological developments supported by theoretical investigation and extensive simulation studies. The project will also develop methods for dynamic risk prediction and evaluate their predictive performance using measures such as time-dependent discrimination and prediction error. The proposed methodology will be applied to large-scale real-world healthcare data from the Clinical Practice Research Datalink (CPRD Aurum) and the UK-PBC cohort. These datasets provide rich longitudinal patient information and clinically important time-to-event outcomes, enabling the proposed methods to be evaluated in realistic healthcare settings. The research will contribute to the development of robust statistical methodology and computational tools for complex healthcare data and may provide a foundation for future research in statistical methodology, dynamic risk prediction and personalised healthcare. The student will receive training in Bayesian statistics, longitudinal and survival analysis, joint modelling, extreme value modelling, and computational statistics. They will also develop skills in simulation studies, reproducible research, scientific writing and presentation. The student will be supported through regular supervision, research group activities, seminars, workshops and relevant doctoral training opportunities, and will be encouraged to disseminate their research through publications and national and international conferences. The project is based within the Probability & Statistics research group at the University of Manchester and offers an opportunity to work at the interface of statistical methodology, data science and health research. Expected outcomes include new statistical methodology, validated computational tools, applications to large-scale real-world healthcare data, and improved approaches for personalised risk prediction. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Natural Sciences Scholarships . If you don’t already have an applicant account, please follow the instructions here . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing your motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility: The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree (or international equivalent) in Statistics, Data Science, Mathematics, or a discipline directly relevant to the PhD OR any upper second-class (2:1) honours degree and a Master's degree at Merit (or international equivalent) in Statistics, Data Science, Mathematics, or a discipline directly relevant to the PhD. Previous research experience in statistics and experience with statistical programming, particularly R or Python is desirable. This project will remain open until filled. If your application is submitted by 1st November 2026, you can expect a decision by 18th December 2026. If your application is submitted by 15th January 2027, you can expect a decision by 30th March 2027. Self or externally funded students can also be considered for this project. FSESoNS

Research areas