SGUL/LSHTM MRC London Intercollegiate Doctoral Training Partnership 2017/18 Potential Phd

SGUL/LSHTM MRC London Intercollegiate Doctoral Training Partnership – 2017/18 Potential PhD Projects

Title of PhD project Investigating post-transplant survival and its determinants in people with cystic fibrosis using a large UK healthcare database and state-of-the-art statistical methods

Supervisor Dr Ruth Keogh LSHTM

Co-Supervisor Dr Aurelien Belot LSHTM

Co-Supervisor Prof Linda Sharples LSHTM

Brief description of project Cystic Fibrosis (CF) is one of the UK’s most common inherited life-shortening diseases, currently affecting around 9000 people, and the median survival age for a person with CF in the UK today is estimated to be 41.5. The UK Cystic Fibrosis Patient Registry is a heath care database which contains longitudinal data on clinical measurements most of the UK CF population.

People with CF can have lung transplants. However, information is lacking on survival prospects for people who receive transplants and some studies have even suggested that transplants may not offer survival benefits. The aim of this project is to use data from the UK Cystic Fibrosis Patient Registry to estimate post-transplant survival, to investigate the factors which are associated with post-transplant survival, and to investigate the impact of transplant as a treatment.

The project will involve the assessment of the suitability of state of the art statistical methods for addressing the questions at hand. There will also be the opportunity to develop method extensions and novel methods where the existing methods have limitations.

The student would join a network of other PhD students and researchers working on a range of projects using the UK Cystic Fibrosis Patient Registry data.

Particular prior educational MSc in Medical Statistics, Statistics, or a related discipline. requirements for a student Strong background in mathematics and statistics, e.g. undertaking this project undergraduate degree in maths/stats or a closely related discipline.

1 SGUL/LSHTM MRC London Intercollegiate Doctoral Training Partnership – 2017/18 Potential PhD Projects

Skills we expect a student Skills in the handling and analysis of large observational to develop/acquire whilst electronic health care databases. In depth knowledge of up to pursuing this project date statistical methods in survival analysis and causal inference and experience of developing and assessing new methods. Collaboration skills involving a network of researchers, including statisticians, clinicians and health economists, working on connected projects, with opportunities.