Methodological Innovations for Analysing Multiple Birth Outcomes in National Perinatal Surveillance

About the project

About the Project Project Description We invite applications for a PhD project focused on developing and evaluating statistical methods for analysing outcomes in multiple pregnancies, using data from the MBRRACE-UK national perinatal surveillance programme. This project addresses key methodological challenges in understanding perinatal mortality among twins and other multiples, with real-world impact on national reporting and clinical practice. Twin pregnancies present unique complexities for surveillance and research. These include linking birth records when maternal identifiers are incomplete, and defining pregnancy-level versus baby-level outcomes when survival differs within the same pregnancy. The successful candidate will explore advanced statistical approaches to tackle these challenges. Key Research Questions How can we reliably link birth records by mother across the UK, especially in devolved nations where identifiers like NHS number and postcode are unavailable? Can baby records be accurately linked without maternal identifiers? How should we analyse pregnancy outcomes for multiples, including cases where one baby survives and the other does not? Key Areas of Exploration Advanced statistical modelling for multiples: Develop and compare approaches for analysing perinatal mortality in twin pregnancies, including hierarchical models, competing risks, and correlated outcomes within pregnancies. Composite and disaggregated outcome measures: Investigate how to represent pregnancy-level outcomes versus baby-level outcomes, and assess implications for benchmarking and interpretation. Handling partial survival: Explore strategies for modelling pregnancies with mixed outcomes (e.g., one neonatal death, one survivor), including sensitivity analyses and alternative risk metrics. Impact on surveillance metrics: Quantify how different analytic choices affect national reporting, risk adjustment, and equity assessments. Record linkage methods: Design and validate algorithms for linking births without maternal identifiers, and evaluate how linkage uncertainty propagates into statistical estimates. Outputs The student will produce peer-reviewed publications, contribute to MBRRACE-UK reports, and develop methodological guidance for analysts and policy-makers. There may also be opportunities to create tools or software packages to support linkage and analysis of multiple births. Candidate Requirements We are looking for a candidate with: Strong quantitative skills in statistics, biostatistics, or epidemiology. Experience with statistical software (e.g., R, Stata, SAS). Interest in applied health research and improving perinatal outcomes. Excellent communication and writing skills. Training and Environment The successful candidate will join a vibrant research environment with access to training in statistical methods, health data science, and research communication. Opportunities for collaboration with clinicians, policy-makers, and statisticians will be available throughout the project. Apply at: https://le.ac.uk/study/research-degrees/research-subjects/school-of-healthcare PhD entry requirements: https://le.ac.uk/study/research-degrees/entry-reqs Supervisor contact details: Prof Bradley Manktelow - brad.manktelow@leicester.ac.uk Dr Lucy Smith - lucy.smith@leicester.ac.uk Dr Ruth Matthews - rjm81@leicester.ac.uk

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