[School of Natural Sciences PhD Scholarships] Causal Inference in Joint Models of Longitudinal Omic Data and Time-to-Event Outcomes

About the project

About the Project The increasing availability of longitudinal omic data provides new opportunities to investigate how biological processes change over time and how these changes may influence subsequent health outcomes. However, establishing causal relationships from such data remains a major statistical challenge. Biological measurements are repeatedly collected and often high-dimensional, while clinical outcomes occur over time and may be affected by censoring, time-varying confounding and complex dependencies between the biological and clinical processes. Existing methods often analyse these components separately, limiting the ability to draw reliable causal conclusions. This PhD will develop a new statistical framework for causal inference from longitudinal omic studies in which biological measurements and time-to-event outcomes are analysed jointly. The research will address an important gap between modern causal inference and statistical methods for longitudinal and survival data, with the aim of providing principled methods for estimating causal effects when both the exposure process and the outcome evolve over time. The project will investigate how causal effects should be defined in longitudinal settings and how they can be estimated when biological measurements are repeatedly observed. Particular attention will be given to time-varying exposures and confounding, informative observation processes, censoring and the dependence between longitudinal measurements and clinical events. Bayesian modelling will provide a flexible framework for quantifying uncertainty and incorporating complex biological information. A further challenge is the high dimensionality of omic data. The research will therefore develop approaches for incorporating large numbers of biological measurements into causal analyses while identifying relevant signals and retaining interpretable models. Different types of omic information may be considered, including taxonomic and functional microbiome measurements. Methodological extensions will be guided by the scientific questions and the structure of the available data rather than by a single predefined modelling strategy. The methods will be evaluated through simulation studies designed to investigate statistical performance under realistic longitudinal and survival settings. They will then be applied to real biomedical data, with a major application using the Baby Biome Study (BBS). This study provides longitudinal microbiome measurements together with detailed maternal, neonatal and clinical information and linked health outcomes, providing an important setting in which to investigate the proposed methodology. Additional biomedical datasets may be used to assess the generalisability of the methods. The project will involve collaboration with researchers in statistics, microbiology, epidemiology and clinical sciences. The successful candidate will receive training in advanced statistical modelling, Bayesian computation, causal inference and the analysis of complex biomedical data, while developing transferable skills in scientific programming and reproducible research. The project will produce new statistical methodology together with reproducible software and applied analyses. Its overall aim is to provide a stronger statistical foundation for drawing causal conclusions from longitudinal omic studies and to improve understanding of how changes in biological processes may contribute to subsequent disease. 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, 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, 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

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