BSc MSc PhD
I am primarily interested in causal inference methodology, to deal with confounding and missing data, in both clinical trials and observational studies. I am particularly interested in how these novel methodologies can be applied to cost-effectiveness analysis.I currently hold an MRC Career Development Award in Biostatistics. Prior to coming to the Department of Medical Statistics at LSHTM, I worked at the Pragmatic Clinical Trial Unit, Centre for Primary Care and Public Health, Queen Mary, as part of a three-year NIHR Methods Fellowship in Medical Statistics.
I have a PhD in Pure Mathematics from Imperial College London (Ergodic Theory) and a MSc in Medical Statistics from the London School of Hygiene and Tropical Medicine.
I am the co-organiser of the School's short courses Statistical Analysis with Missing Data using Multiple Imputation and Inverse Probability Weighting and Causal Inference in Epidemiology: Recent Methodological Developments
I am also co-organiser of the two Bayesian Modules in the MSc in Medical Statistics.
I am currently working in developing methods for estimating causal treatment effect when there is departures from protocol in a randomised trial (i.e. non-compliance and missing data) using Multiple Imputation. This work is in collaboration with Prof James Carpenter.
Another strand of my current work involves doubly-robust estimators, in particular, those that can be paired with the use of ensemble machine learning methods, e.g. Targeted Minimum Loss estimators (TMLE). These methods are very promising when dealing with big data.I have also worked in methods to analyse missing clustered data. Currently, I study and compare the performance of different multiple imputation techniques to handle whole clusters non-response (empty clusters). I am also interested in extending such methods for cost-effectiveness analysis, accounting for the bivariate nature of the endpoints.
I am a member of the Centre for Statistical Methodology, and one of the co-ordinators of the missing data and causal inference themes.