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Professor Mark Gilthorpe

Professor

Mark is a Professor of Statistical Epidemiology in the Obesity Institute, Leeds Beckett University. He is also a Fellow of the Alan Turing Institute.

Trained as a mathematical physicist, Mark's driving interest centres on improving our understanding of the observable world through modelling. After his PhD, Mark worked as a Consultant Data Analyst before entering academia and has since fashioned a programme of interdisciplinary research that spans the gap between theoretical and applied data analytics. He focuses on modelling complexity and highlighting and solving common analytical problems in observational research.

Mark's research and teaching interests have converged around the insights and utility of causal inference methods in observational research, especially how causal methods might be integrated with machine learning and AI to better understand and model complex systems.

Research Interests

Mark is currently seeking to understand complex relationships between individuals within their natural environment through the development and application of observational methods, specifically through the integration of causal inference modelling and agent-based modelling. His applied domain for this challenge is the causes and consequences of obesity within our society.

Carnegie School of Sport

Ask Me About

Causal Inference
Observational Methods
Statistics
Body image
Cancer
Diet
Health
Nutrition
Obesity
Public health
Sociology
Sport science

Selected Outputs

Iob E; Pingault J-B; Munafò MR; Stubbs B; Gilthorpe MS; Maihofer AX; PGC-PTSD; Danese A (2023) Testing the causal relationships of physical activity and sedentary behaviour with mental health and substance use disorders: a Mendelian randomisation study. Molecular Psychiatry, pp. 1-15.

https://doi.org/10.1038/s41380-023-02133-9
Lonnie M; Hunter E; Stone RA; Dineva M; Aggreh M; Greatwood H; Johnstone AM; FIO Food team (2023) Food insecurity in people living with obesity: Improving sustainable and healthier food choices in the retail food environment-the FIO Food project. Nutrition Bulletin, pp. 1-10.

https://doi.org/10.1111/nbu.12626
Tomova GD; Gilthorpe MS; Tennant PWG (2022) Theory and performance of substitution models for estimating relative causal effects in nutritional epidemiology. American Journal of Clinical Nutrition, pp. 1-10.

https://doi.org/10.1093/ajcn/nqac188
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),

https://doi.org/10.1371/journal.pone.0263432
Gadd SC; Comber A; Gilthorpe MS; Suchak K; Heppenstall AJ (2022) Simplifying the interpretation of continuous time models for spatio-temporal networks. Journal of Geographical Systems, 24 (2), pp. 171-198.

https://doi.org/10.1007/s10109-020-00345-z
Tomova GD; Arnold KF; Gilthorpe MS; Tennant PWG (2022) Adjustment for energy intake in nutritional research: a causal inference perspective. The American Journal of Clinical Nutrition, 115 (1), pp. 189-198.

https://doi.org/10.1093/ajcn/nqab266
Gadd SC; Comber A; Tennant P; Gilthorpe MS; Heppenstall AJ (2022) The utility of multilevel models for continuous-time feature selection of spatio-temporal networks. Computers, Environment and Urban Systems, 91

https://doi.org/10.1016/j.compenvurbsys.2021.101728
Tennant PWG; Arnold KF; Ellison GTH; Gilthorpe MS (2021) Analyses of ‘change scores’ do not estimate causal effects in observational data. International Journal of Epidemiology

https://doi.org/10.1093/ije/dyab050
Mbotwa JL; Kamps MD; Baxter PD; Ellison GTH; Gilthorpe MS (2021) Latent class regression improves the predictive acuity and clinical utility of survival prognostication amongst chronic heart failure patients. PloS one, 16 (5),

https://doi.org/10.1371/journal.pone.0243674
Tennant PWG; Murray EJ; Arnold KF; Berrie L; Fox MP; Gadd SC; Harrison WJ; Keeble C; Ranker LR; Textor J (2021) Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. International Journal of Epidemiology, 50 (2), pp. 620-632.

https://doi.org/10.1093/ije/dyaa213
Wilkinson J; Arnold KF; Murray EJ; van Smeden M; Carr K; Sippy R; de Kamps M; Beam A; Konigorski S; Lippert C (2020) Time to reality check the promises of machine learning-powered precision medicine. The Lancet Digital Health, 2 (12), pp. E677-E680.

https://doi.org/10.1016/s2589-7500(20)30200-4
Arnold KF; Davies V; de Kamps M; Tennant PWG; Mbotwa J; Gilthorpe MS (2020) Reflection on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning. International Journal of Epidemiology, 49 (6), pp. 2074-2082.

https://doi.org/10.1093/ije/dyaa049
Arnold KF; Berrie L; Tennant PWG; Gilthorpe MS (2020) A causal inference perspective on the analysis of compositional data. International Journal of Epidemiology, 49 (4), pp. 1307-1313.

https://doi.org/10.1093/ije/dyaa021
Gadd SC; Tennant PWG; Heppenstall AJ; Boehnke JR; Gilthorpe MS (2019) Analysing trajectories of a longitudinal exposure: A causal perspective on common methods in lifecourse research. PLoS ONE, 14 (12),

https://doi.org/10.1371/journal.pone.0225217