Statistical modelling of disease progression in Spinal Muscular Atrophy and Duchenne Muscular Dystrophy
Supervisors: Professor Francesco Muntoni, Dr Giovanni Baranello, Ms Deborah Ridout
This project addresses an urgent issue, which relates to the need to collect and analyse big data sources on the natural history disease progression of children with Spinal Muscular Atrophy (SMA) and Duchenne Muscular Dystrophy DMD). Currently some treatments are available for both these conditions in the UK, and many clinical trials are ongoing, with further therapeutic options on the way. There is a strong need to understand how different aspects, including motor function, body composition and growth patterns evolve, both in untreated and in treated patients. A better understanding of the natural history of SMA and DMD patients, especially the evolution of more novel considerations such as body composition, nutritional and metabolic status as well as motor function, can help in improving the clinical management of patients and their comorbidities, assessing efficacy of treatments, profiling disease progression, and designing clinical trials.
Aims & Objectives
The aim of the project is to collate and use advanced statistical modelling to analyse both retrospective and prospective data on body composition, growth patterns, nutritional status, motor and respiratory function in DMD and SMA untreated patients, in order to provide natural history data to assess the effects of different therapeutics options.
Approaches & Methods
With access to multiple data sources, some collected as part of large prospective longitudinal studies in these two population of patients at Great Ormond Street Hospital and others from national and international network collaborations, there is a wealth of opportunities to build on previous research in this field. Approaches such as longitudinal trajectory analysis and growth modelling could form the basis of this project. Other objectives could include modelling time to event data, exploration of prognostic models or consideration of more novel applications of latent class models, in order to provide essential information for evaluating the impact of the underlying conditions and comorbidities on clinical outcomes.
The studentship will provide training in all required aspects of research methodology (study design, set-up, statistical analysis, data science). Different aspects of the research could be further developed depending on the student’s background and interests. The supervisory team has combined experience in all aspects of the proposed research and the student will be embedded within both the Neurosciences and Population, Policy and Practice Research and Teaching Departments. The student will also benefit from the biostatistical networks in place within UCL and GOSH ICH.
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