Prof Elizabeth Williamson
Prof of Biostats & Health Data Science
United Kingdom
I studied mathematics at King's College Cambridge, followed by a Masters degree in Medical Statistics at the University of Leicester. I then moved to London to begin a PhD at LSHTM under the supervision of James Carpenter, which was awarded in 2007. For the next 7 years I worked in Australia, beginning at the NHMRC Clinical Trials Unit at Sydney University, then moving to the Murdoch Childrens Research Institute in Melbourne, followed by positions at Monash University and the University of Melbourne. In 2014, I returned to LSHTM. Since then, my work has focused on the use of electronic health records data for health research. Initially, the work focused on missing data within causal analyses. During the COVID-19 pandemic, I was involved in the OpenSAFELY collaborative, a new platform for linked primary care record data, and contributed to using these data to answer a number of urgent health related questions through the pandemic. Since then, my methodological interests have broadened to encompass statistical challenges of both causal analyses and risk prediction using data from electronic health records.
Affiliations
Centres
Teaching
I led the development of the new MSc in Health Data Science, which will have its first student intake in September 2020. I acted as Programme Director for the initial phases - securing funding, assembling the development team and mapping out the curriculum. As part of this, I co-developed the Data Challenge and Statistics for Health Data Science modules. I am currently the module organiser for the module Analysis of Electronic Health Record data, which I also co-developed.
I have successfully supervised five doctoral students to completion and currently supervise three doctoral students.
Research
I am particularly interested in the methods used to make causal statements and to generate clinical risk predictions using routinely collected data. These sorts of data bring many challenges, including measured and unmeasured confounding, time-varying exposures and confounding (for causal analyses), changes in medication use over time and measurement error. A huge range of statistical techniques have been proposed to deal with these challenges, including extended implementations of multiple imputation, propensity scores, marginal structural models, series of sequential trials, for the causal analyses, and causal risk prediction approaches for risk prediction. Understanding whether these techniques provide valid results in real life scenarios, and identifying situations in which the simpler approaches are sufficient is critical to allow researchers to draw valid and robust conclusions from these data.
Key collaborators at LSHTM include: James Carpenter, Ian Douglas, Clemence Leyrat, John Tazare and Liam Smeeth.
Selected Publications
