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Kellyn F Arnold

Showing results (1-10 of 11) with videos related to

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International Journal of Epidemiology|December 7, 2018
DAG-informed regression modelling, agent-based modelling and microsimulation modelling: a critical comparison of methods for causal inferenceKellyn F Arnold, Wendy J Harrison, Alison J Heppenstall, et al.
International Journal of Epidemiology|March 11, 2020
A causal inference perspective on the analysis of compositional dataKellyn F Arnold, Laurie Berrie, Peter W G Tennant, et al.
The American Journal of Clinical Nutrition|July 27, 2021
Adjustment for energy intake in nutritional research: a causal inference perspectiveGeorgia D Tomova, Kellyn F Arnold, Mark S Gilthorpe, et al.
The American Journal of Clinical Nutrition|June 22, 2022
Reply to WC Willett et alGeorgia D Tomova, Kellyn F Arnold, Mark S Gilthorpe, et al.
International Journal of Epidemiology|June 8, 2021
Analyses of 'change scores' do not estimate causal effects in observational dataPeter W G Tennant, Kellyn F Arnold, George T H Ellison, et al.
American Journal of Epidemiology|June 25, 2024
Depicting deterministic variables within directed acyclic graphs: an aid for identifying and interpreting causal effects involving derived variables and compositional dataLaurie Berrie, Kellyn F Arnold, Georgia D Tomova, et al.
International Journal of Epidemiology|May 8, 2020
Reflection on modern methods: generalized linear models for prognosis and intervention-theory, practice and implications for machine learningKellyn F Arnold, Vinny Davies, Marc de Kamps, et al.
Plos One|April 14, 2022
Estimating the effects of lockdown timing on COVID-19 cases and deaths in England: A counterfactual modelling studyKellyn F Arnold, Mark S Gilthorpe, Nisreen A Alwan, et al.
BMC Medical Research Methodology|March 23, 2025
Simulating hierarchical data to assess the utility of ecological versus multilevel analyses in obtaining individual-level causal effectsLydia Kakampakou, Jonathan Stokes, Andreas Hoehn, et al.
The Lancet. Digital Health|December 17, 2020
Time to reality check the promises of machine learning-powered precision medicineJack Wilkinson, Kellyn F Arnold, Eleanor J Murray, et al.
Pageof 2

Showing results (1-10 of 11) with videos related to

Sort By:
Pageof 2
International Journal of Epidemiology|December 7, 2018
DAG-informed regression modelling, agent-based modelling and microsimulation modelling: a critical comparison of methods for causal inferenceKellyn F Arnold, Wendy J Harrison, Alison J Heppenstall, et al.
International Journal of Epidemiology|March 11, 2020
A causal inference perspective on the analysis of compositional dataKellyn F Arnold, Laurie Berrie, Peter W G Tennant, et al.
The American Journal of Clinical Nutrition|July 27, 2021
Adjustment for energy intake in nutritional research: a causal inference perspectiveGeorgia D Tomova, Kellyn F Arnold, Mark S Gilthorpe, et al.
The American Journal of Clinical Nutrition|June 22, 2022
Reply to WC Willett et alGeorgia D Tomova, Kellyn F Arnold, Mark S Gilthorpe, et al.
International Journal of Epidemiology|June 8, 2021
Analyses of 'change scores' do not estimate causal effects in observational dataPeter W G Tennant, Kellyn F Arnold, George T H Ellison, et al.
American Journal of Epidemiology|June 25, 2024
Depicting deterministic variables within directed acyclic graphs: an aid for identifying and interpreting causal effects involving derived variables and compositional dataLaurie Berrie, Kellyn F Arnold, Georgia D Tomova, et al.
International Journal of Epidemiology|May 8, 2020
Reflection on modern methods: generalized linear models for prognosis and intervention-theory, practice and implications for machine learningKellyn F Arnold, Vinny Davies, Marc de Kamps, et al.
Plos One|April 14, 2022
Estimating the effects of lockdown timing on COVID-19 cases and deaths in England: A counterfactual modelling studyKellyn F Arnold, Mark S Gilthorpe, Nisreen A Alwan, et al.
BMC Medical Research Methodology|March 23, 2025
Simulating hierarchical data to assess the utility of ecological versus multilevel analyses in obtaining individual-level causal effectsLydia Kakampakou, Jonathan Stokes, Andreas Hoehn, et al.
The Lancet. Digital Health|December 17, 2020
Time to reality check the promises of machine learning-powered precision medicineJack Wilkinson, Kellyn F Arnold, Eleanor J Murray, et al.
Pageof 2