MissEllie Van Vogt

Ellie is an NHIR Doctoral Fellow at the Imperial Clinical Trials Unit (ICTU). Their work focuses on using causal machine learning (CML) methods to estimate heterogeneous treatment effects (HTEs) in randomised controlled trials, with a focus on intensive care settings. Their PhD will establish guidelines for the use of CML in trials, extend current methods to address competing risks, and explore avenues for internal validation of HTEs in trials.

Ellie joined ICTU in 2019 as an NIHR Research Methods intern during their undergraduate Mathematics degree at the University of Oxford. After completing their degree, Ellie rejoined ICTU as a systematic review coordinator for the SCiPAD study. Ellie was awarded an NIHR Pre-Doctoral Fellowship in 2020 and, through this, completed an MSc in Health Data Analytics and Machine Learning at Imperial. In the second year of the fellowship, Ellie researched CML methods to estimate HTEs, with application to the VANISH trial. Between Fellowships, Ellie was a statistician for a Cochrane network meta-analysis of topical interventions for the treatment of eczema, and a research assistant on a CML for HTEs project in collaboration with the Alan Turing Institute and the MRC Clinical Trials Unit.