Bio
Georgia was a health data science doctoral student registered with the Leeds Institute for Data Analytics at the University of Leeds. Her main interests are within the fields of epidemiology and causal inference methods.
Her background includes a BSc in Nutrition from the University of Leeds, followed by an MSc in Health Data Analytics during which she was introduced to causal inference methods and developed a keen interest in this area. As part of this, she co-leads the Causal Inference Interest Group at the Institute and has significant experience in teaching causal inference methods to health and social scientists of all career levels.
Research interests
Georgia's doctoral research used causal diagrams and data simulations to examine different approaches to analysing and interpreting compositional data in the field of nutritional epidemiology. In the context of energy intake adjustment, their research involved placing different adjustment methods in a causal framework, exploring their robustness to specific forms of confounding, and proposing a flexible approach that can be used to robustly estimate a variety of potential estimands. In the context of food substitution modelling, they explored the performance of the different modelling approaches, in particular when the component variables are not in the same units. Finally, they examined the performance of regression-based modelling approaches in the presence of non-linear relationships, and how they compare to established ‘gold standard’ methods.
Selected publications and papers
Published Papers:
1. Tomova GD, Gilthorpe MS, Tennant PWG. Theory and performance of substitution models for estimating relative causal effects in nutritional epidemiology. 2022. Am J Clin Nutr 116(5):1379-88.
2. Tomova GD, Arnold KF, Gilthorpe MS, Tennant PWG. Adjustment for energy intake in nutritional research: a causal inference perspective. 2022. Am J Clin Nutr 115(1):189-98.
3. Arnold KF, Gilthorpe MS, Alwan NA, Heppenstall AJ, Tomova GD, McKee M, Tennant PWG. 2022. Estimating the effects of lockdown timing on COVID-19 cases and deaths in England: A counterfactual modelling study. PLoS ONE 17(4):e0263432.
4. Tennant PWG, Murray EJ, Arnold KF, Berrie L, Fox MP, Gadd SC, Harrison WJ, Keeble C, Ranker LR, Textor J, Tomova GD, Gilthorpe MS, Ellison GTH. Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. 2021. Int J Epidemiol 50(2):620-32.
Pre-prints:
1. Tennant PWG, Tomova GD, Murray EJ, Arnold KF, Fox MP, Gilthorpe MS. 2023. Lord’s ‘paradox’ explained: the 50-year warning on the use of ‘change scores’ in observational data. (arXiv)
2. Berrie L, Arnold KF, Tomova GD, Gilthorpe MS, Tennant PWG. 2022. Depicting deterministic variables within directed acyclic graphs (DAGs): An aid for identifying and interpreting causal effects involving tautological associations, compositional data, and composite variables. (arXiv)
Oral and poster presentations at the following conferences:
American Causal Inference Conference (2022)
International Biometric Conference (2022)
Society for Epidemiologic Research (USA) Annual Meeting (2019, 2022, 2023)
Society for Social Medicine & Population Health UK (2021, 2022, 2023)