My research is funded by a UK Research and Innovation (UKRI) Future Leaders Fellowship on the topic of Evaluating effects of complex treatments in chronic disease using large observational datasets.

The aim of my research programme is to apply and develop statistical causal inference methods for answering questions about the effects of treatments on health outcomes using observational data. I am especially interested on an approach based on mimicking a sequence of “hypothetical trials” within longitudinal data. 

I am using these methods to tackle crucial questions about treatment effects in cystic fibrosis (CF) using data from the UK Cystic Fibrosis Registry. This includes investigations of the impact of new precision medicines on long term outcomes, and of the impact of different treatment combinations.

I am also involved in Covid-19 research, including to understand the trajectories of patients hospitalised with Covid-19 and to evaluate the effectiveness of different treatment strategies.

• Methods for the analysis of survival or time-to-event data in general.
• Methods for dynamic prediction of survival using large patient databases using  landmarking, including using machine learning approaches.
• Use of multiple imputation to handle missing data in case-control studies and time-to-event studies.
• Methods for correcting for the effects of exposure measurement error.