[School of Natural Sciences PhD Scholarships] Joint Modelling of Longitudinal Outcomes and Multistate Survival Processes for Dynamic Prediction of Disease Progression

About the project

About the Project Longitudinal studies in health and ageing research routinely collect repeated measurements of health outcomes alongside information on changes in clinical and functional status. These processes are often interdependent, reflecting common underlying characteristics and the progression of ageing and disease. Analysing them separately may therefore provide an incomplete picture of how health changes over time, particularly when dropout or mortality is related to an individual's health status. This PhD will develop new statistical methods for jointly analysing repeated health measurements and changes between multiple health states. The research will investigate how information collected repeatedly from individuals can be combined with their changing health status to provide a more complete understanding of disease and ageing processes. A primary application will use the English Longitudinal Study of Ageing (ELSA). The project will focus on delayed recall as a repeated measure of cognitive function and frailty as a progression through four clinically meaningful states: non-frail, pre-frail, frail and death. The research will investigate how patterns of cognitive change are related to progression between these states and whether repeated cognitive measurements can provide useful information about an individual's future health status. An important aspect of the project will be to investigate how predictions of future health can be updated as new information becomes available. This could provide a more detailed understanding of individual trajectories of ageing and help identify differences in the progression of cognitive decline and frailty between individuals. The research will combine methodological statistical development with simulation studies and applications to real-world ageing data. The methods will be evaluated under a range of realistic scenarios before being applied to ELSA. Reproducible statistical software will also be developed to support the wider application of the proposed methods. The successful candidate will receive training in Bayesian statistics, longitudinal data analysis, survival and multistate modelling, joint modelling and computational statistics. They will also develop transferable skills in statistical programming, simulation studies and reproducible analysis of complex health and population data. The project will produce new statistical methodology, computational tools and applied analyses. By jointly considering cognitive change and progression through different health states, the research aims to provide new insights into the interconnected processes of cognitive decline, frailty and mortality in ageing populations. 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, Mathematics, Data Science, Biostatistics, 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, Mathematics, Data Science, Biostatistics, or a discipline directly relevant to the PhD. Previous research experience in statistical programming, particularly in R or a similar language 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