Methodological Innovations for Analysing Multiple Birth Outcomes in National Perinatal Surveillance

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