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Georgia D Tomova
Georgia D Tomova
Verified email at leeds.ac.uk
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Year
Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations
PWG Tennant, EJ Murray, KF Arnold, L Berrie, MP Fox, SC Gadd, ...
International journal of epidemiology 50 (2), 620-632, 2021
6372021
Adjustment for energy intake in nutritional research: a causal inference perspective
GD Tomova, KF Arnold, MS Gilthorpe, PWG Tennant
The American journal of clinical nutrition 115 (1), 189-198, 2022
912022
Theory and performance of substitution models for estimating relative causal effects in nutritional epidemiology
GD Tomova, MS Gilthorpe, PWG Tennant
The American Journal of Clinical Nutrition 116 (5), 1379-1388, 2022
252022
Estimating the effects of lockdown timing on COVID-19 cases and deaths in England: A counterfactual modelling study
KF Arnold, MS Gilthorpe, NA Alwan, AJ Heppenstall, GD Tomova, ...
PLoS One 17 (4), e0263432, 2022
182022
Reply to WC Willett et al.
GD Tomova, KF Arnold, MS Gilthorpe, PWG Tennant
The American Journal of Clinical Nutrition 116 (2), 609-610, 2022
62022
Depicting deterministic variables within directed acyclic graphs (DAGs): An aid for identifying and interpreting causal effects involving derived variables and compositional data
L Berrie, KF Arnold, GD Tomova, MS Gilthorpe, PWG Tennant
American Journal of Epidemiology, kwae153, 2024
4*2024
Lord's' paradox'explained: the 50-year warning on the use of'change scores' in observational data
PWG Tennant, GD Tomova, EJ Murray, KF Arnold, MP Fox, MS Gilthorpe
arXiv preprint arXiv:2302.01822, 2023
2*2023
Meta-analyses of nutritional exposures must identify and distinguish between study estimands: an illustrative review
N Ortega, PWG Tennant, CC Dahm, D Tobias, DC Greenwood, DB Ibsen, ...
OSF, 2023
2023
Causal Inference with Compositional Data: Using directed acyclic graphs and data simulations to understand and model robust causal effects in nutritional and time-use epidemiology
GD Tomova
University of Leeds, 2023
2023
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