Multimorbidity in paediatric rheumatic disease trials: life-course patterns, treatment response and outcomes using individual participant-level data
Supervisors:
Prof Stefan Siebert, School of Infection & Immunity, University of Glasgow
Dr Eve Smith, School of Infection & Immunity, University of Glasgow
Prof David McAllister, School of Health & Wellbeing, University of Glasgow
Dr Sohan Seth, School of Informatics, University of Edinburgh
Summary:
Rheumatic and musculoskeletal diseases (RMDs) are a major cause of chronic illness, pain and reduced quality of life in children and young people. These conditions are increasingly recognised as part of a broader burden of multiple long-term conditions (MLTCs), where autoimmune, metabolic, mental health and treatment-related comorbidities interact across the life-course, yet this complexity remains poorly understood in paediatric populations.
This PhD offers a unique opportunity to work with large, high-quality international clinical trial datasets, combining individual participant-level data (IPD) from rigorously conducted randomised controlled trials via the global Vivli platform. These richly characterised datasets enable powerful analyses not possible in single studies.
You will develop a novel framework to measure multimorbidity, identify clinically meaningful clusters using advanced data-driven and Bayesian methods, and examine how these influence treatment response, safety and long-term outcomes in childhood and adulthood.
With a strong patient-centred and health inequalities focus, and close collaboration with children, young people and families, this project will translate findings into improved clinical decision-making.
You will gain cutting-edge training in data science, epidemiology and MLTC research, positioning you at the forefront of modern translational health research.