Zobrazeno 1 - 10 of 59 pro vyhledávání: '"Peter W G Tennant"'
1
Akademický článek
Estimating the effects of lockdown timing on COVID-19 cases and deaths in England: A counterfactual modelling study.
Autor: Kellyn F Arnold, Mark S Gilthorpe, Nisreen A Alwan, Alison J Heppenstall, Georgia D Tomova, Martin McKee, Peter W G Tennant
Publikováno v: PLoS ONE, Vol 17, Iss 4, p e0263432 (2022)
BackgroundDuring the first wave of the COVID-19 pandemic, the United Kingdom experienced one of the highest per-capita death tolls worldwide. It is debated whether this may partly be explained by the relatively late initiation of voluntary social dis
Externí odkaz: https://doaj.org/article/65cd3a0996324ab5a359fde4d6988082
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2
Akademický článek
Time to reality check the promises of machine learning-powered precision medicine
Autor: Jack Wilkinson, PhD, Kellyn F Arnold, PhD, Eleanor J Murray, ScD, Maarten van Smeden, PhD, Kareem Carr, MSc, Rachel Sippy, PhD, Marc de Kamps, PhD, Andrew Beam, PhD, Stefan Konigorski, PhD, Christoph Lippert, ProfPhD, Mark S Gilthorpe, ProfPhD, Peter W G Tennant, PhD
Publikováno v: The Lancet: Digital Health, Vol 2, Iss 12, Pp e677-e680 (2020)
Summary: Machine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses to therapies. If successful, this strategy would re
Externí odkaz: https://doaj.org/article/ba0bc357105946ffbe5e017ed738eb9e
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3
Akademický článek
Analysing trajectories of a longitudinal exposure: A causal perspective on common methods in lifecourse research.
Autor: Sarah C Gadd, Peter W G Tennant, Alison J Heppenstall, Jan R Boehnke, Mark S Gilthorpe
Publikováno v: PLoS ONE, Vol 14, Iss 12, p e0225217 (2019)
Longitudinal data is commonly analysed to inform prevention policies for diseases that may develop throughout life. Commonly methods interpret the longitudinal data as a series of discrete measurements or as continuous patterns. Some of the latter me
Externí odkaz: https://doaj.org/article/449b26120ad04ad9b113c317b13f64c5
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4
Causal and Associational Language in Observational Health Research: A Systematic Evaluation
Autor: Cathrine Axfors, Arthur Chatton, Elizabeth A. Stuart, Ariadne E. Rivera Aguirre, Julia M. Rohrer, Ian Schmid, Palwasha Khan, Daloha Rodríguez-Molina, Sebastián Peña, Sophie Pilleron, Camila Olarte Parra, Mark Kelson, Saman Khalatbari-Soltani, Jessie Seiler, Mi-Suk Kang Dufour, Eleanor J Murray, Peter W. G. Tennant, Anna Booman, Meg G. Salvia, Daniel J. Dunleavy, Taym M. Alsalti, Thomas Rhys Evans, Philipp Schoenegger, Rachel A. Hoopsick, Sarah Wieten, Sze Tung Lam, Gideon Meyerowitz-Katz, Stefanie Do, Rebekah Baglini, Sarah E. Twardowski, Sarah J Howcutt, Matthew P. Fox, Mari Takashima, Onyebuchi A. Arah, Julia Dabravolskaj, Clemence Leyrat, Emily Riederer, Shashank Suresh, Ashley L. O’Donoghue, Alberto Antonietti, Noah Haber, Eric Au, Nnaemeka U. Odo, Taylor McLinden, José Andrés Calvache, Alison E. Simmons, Talal S. Alshihayb, Nicholas Judd, Andreea Steriu
Publikováno v: Haber, N, Wieten, S, Rohrer, J, Arah, O, Tennant, P, Stuart, E, Murray, E, Pilleron, S, Lam, S T, Riederer, E, Howcutt, S J, Simmons, A, Leyrat, C, Schoenegger, P, Booman, A, Dufour, M-S K, O'Donoghue, A & Baglini, R B 2022, ' Causal and associational language in observational health research: A systematic evaluation ', American Journal of Epidemiology . https://doi.org/10.1101/2021.08.25.21262631
Background Avoiding “causal” language with observational study designs is common publication practice, often justified as being a more cautious approach to interpretation. Objectives We aimed to i) estimate the degree to which causality was impli
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b2a830009842e8233432bca87c78c52
https://escholarship.org/uc/item/5c56c6gk
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5
Choice of Outcome in COVID-19 Studies and Implications for Policy: Mortality and Fatality
Autor: Peter W. G. Tennant, Eleanor J Murray, Maarten van Smeden, Daniel Westreich, Jessie K. Edwards
Publikováno v: American Journal of Epidemiology
In this brief communication, we discuss the confusion of mortality with fatality in the interpretation of evidence in the coronavirus disease 2019 (COVID-19) pandemic, and how this confusion affects the translation of science into policy and practice
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f5ed928cbf8062819a92338e613d79de
http://europepmc.org/articles/PMC8513401
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6
Analyses of ‘change scores’ do not estimate causal effects in observational data
Autor: Peter W. G. Tennant, George T. H. Ellison, Mark S. Gilthorpe, Kellyn F Arnold
Background: In longitudinal data, it is common to create 'change scores' by subtracting measurements taken at baseline from those taken at follow-up, and then to analyse the resulting 'change' as the outcome variable. In observational data, this appr
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a3bd2e6806459acf70d2e722ecf16f8
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7
Adjustment for energy intake in nutritional research: a causal inference perspective
Autor: Mark S. Gilthorpe, Georgia D Tomova, Peter W. G. Tennant, Kellyn F Arnold
BackgroundFour models are commonly used to adjust for energy intake when estimating the causal effect of a dietary component on an outcome; (1) the ‘standard model’ adjusts for total energy intake, (2) the ‘energy partition model’ adjusts for
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71522c719f8051b0517ac48f987d69a8
https://doi.org/10.1101/2021.01.20.21250156
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8
Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations
Autor: Laurie Berrie, George T. H. Ellison, Eleanor J Murray, Kellyn F Arnold, Georgia D Tomova, Peter W. G. Tennant, Wendy J Harrison, Sarah C. Gadd, Johannes Textor, Matthew P. Fox, Mark S. Gilthorpe, Claire Keeble, Lynsie R Ranker
Publikováno v: International Journal of Epidemiology, 50, 2, pp. 620-632
International Journal of Epidemiology
International Journal of Epidemiology, 50, 620-632
Background Directed acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require conditioning when estimating causal effects. This review examined the use of DAGs in applied health research to inform r
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2bc8c0c61c39545a47ace07156403ff1
https://repository.ubn.ru.nl/handle/2066/244722
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9
Reflection on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning
Autor: Marc de Kamps, John Mbotwa, Vinny Davies, Peter W. G. Tennant, Mark S. Gilthorpe, Kellyn F Arnold
Publikováno v: International Journal of Epidemiology
Prediction and causal explanation are fundamentally distinct tasks of data analysis. In health applications, this difference can be understood in terms of the difference between prognosis (prediction) and prevention/treatment (causal explanation). Ne
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8433bb0174d57fb62749464e8d73dbaa
https://eprints.gla.ac.uk/252041/1/252041.pdf
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