3x fully-funded PhD Studentships are available as part of the MRC funded London Intercollegiate Doctoral Training Partnership (MRC LID) between the Population Health Research Institute at St. George’s, University of London (SGUL) and the London School of Hygiene and Tropical Medicine (LSHTM).


1)  “Retinal vessel size and shape associations with brain structure and function”, supervised by Professor Alicja Rudnicka (SGUL), Professor Christopher Owen (SGUL) and Professor Chris Frost (LSHTM).


This project will make use of UK Biobank data containing a large number of retinal vasculometry measures and vessel maps in 80,000 participants of which 20,000 participants also underwent brain imaging. In order to approach the primary research question, “Are morphological features/changes of the retinal vessels predictive of poorer cognitive function and/or dementia later in life?” a number of complex statistical methods will be required.

Due to the magnitude of retinal imaging variables within the UK Biobank dataset, data reduction techniques, e.g. factor analysis, will be crucial for identifying summary statistics of retinal vessels. Prognostic models will be developed for the prediction of cognitive scores utilising the summarised retinal imaging variables as candidate predictors within the models. The assessment of non-linear associations will be crucial within the model development stage. Techniques such as bootstrapping and cross-validation will be utilised for the model validation where assessments of model calibration and discrimination will be determined. TRIPOD guidance for development and validation of prediction models will be followed throughout this project.



2) “Using data from EUROmediCAT to develop methods to identify medications that might harm the fetus if taken during the first trimester of pregnancy”, supervised by Professor Joan Morris (SGUL), Professor Stephen Evans (LSHTM) and Dr Iain Carey (SGUL).


Many women take medications during the first trimester of pregnancy, but there is a lack of evidence about the safety of these medications to the fetus. Harmful medications may cause multiple different birth defects in the fetus. However, previous methods to identify harmful medications have concentrated on identifying single birth defects that are associated with specific medications. This project aims to develop statistical methods to identify groups of birth defects that might be associated with specific medications. Existing data on over 33,000 pregnancies from the EUROmediCAT database will be used to develop the statistical methodology, which is likely to include multiple comparison procedures, Bayesian hierarchical models and cluster analysis.



3) “Epidemiological studies on environmental risk factors using big data methods” supervised by Dr Antonio Gasparrini (LSHTM), Professor Richard Atkinson (SGUL) and Dr Ana Vicedo-Cabrera (LSHTM)


There is an increased interest on the impacts of environmental factors, such as air pollution and extreme temperature, on human health. The availability of new big data technologies offer tremendous opportunities to advance knowledge in this research area. This PhD project aims at extending epidemiological research on environment-health associations in the UK using large data resources, including small-area/individual health outcomes and high-resolution exposure maps. Statistical techniques which will be used include: big data analyses approaches, regression modelling, spatio-temporal modelling and statistical computing.


For further information and to apply, please visit http://mrc-lid.lshtm.ac.uk/2020-21-quantitative-skills-for-large-data-sets-projects/