Mark Steven Gilthorpe

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Professor of Statistical Epidemiology

Leeds Beckett University

United Kingdom

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About

Mark Steven Gilthorpe is a Professor of Statistical Epidemiology at Leeds Beckett University in the UK. His research focuses on statistical methodologies in epidemiology, exploring causal relationships in various fields including nutrition and mental health. Recent articles highlight his work on causal analysis of weight outcomes, eye-tracking tools in sports, and advancements in multilevel analyses. He is committed to enhancing the understanding of complex data through innovative statistical approaches.

Recent Grants

Grant: Close

Generalising Data Science

Open Date: 2023-04-01

Close Date: 2024-09-01

Grant: Close

FIO-FOOD, Food Insecurity in people living with Obesity - improving sustainable and healthier food choices in the retail FOOD environment.

Open Date: 2022-08-31

Close Date: 2025-08-30

Grant: Close

To develop a smart clinical reasoning tool for automatic diagnosis and causal inference of cat/dog skin conditions through a smartphone app

Open Date: 2021-03-01

Close Date: 2023-06-01

Grant: Close

MRes in Data Science and Analytics for Health

Open Date: 2020-10-01

Close Date: 2024-09-01

Grant: Open

Application of functional MRI to improve assessment of chronic kidney disease (AFiRM study)

Open Date: 2020-09-01

Close Date: 2027-02-01

Articles (14)

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2021

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2021

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2021

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2020

Collaborators (20)

Richard Allmendinger

Professor at Faculty of Humanities Doctoral Academy, Alliance Manchester Business School

The University of Manchester

UNITED KINGDOM

Mark Steven Gilthorpe

Research Interests

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About

Recent Grants

Articles (14)

Composite Variable Bias: Causal Analysis of Weight Outcomes

Simulating hierarchical data to assess the utility of ecological versus multilevel analyses in obtaining individual-level causal effects

Depicting deterministic variables within directed acyclic graphs: an aid for identifying and interpreting causal effects involving derived variables and compositional data

Assessment of an eye-tracking tool to discriminate between concussed and not concussed professional male rugby players: a cohort study

Testing the causal relationships of physical activity and sedentary behaviour with mental health and substance use disorders: a Mendelian randomisation study

Demonstration of functional rehabilitation treatment effects in children and young people after severe acquired brain injury

Theory and performance of substitution models for estimating relative causal effects in nutritional epidemiology

Reply to WC Willett et al.

Simplifying the interpretation of continuous time models for spatio-temporal networks

Adjustment for energy intake in nutritional research: a causal inference perspective

Early childhood weight gain: Latent patterns and body composition outcomes.

Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations

Distinct Body Mass Index Trajectories to Young-Adulthood Obesity and Their Different Cardiometabolic Consequences

Time to reality check the promises of machine learning-powered precision medicine

Collaborators (20)

Richard Allmendinger

Andreas Höhn

Georgia Tomova

James Brown

Leah Li

lorna wales

Rob Forsyth

Peter Tennant

Andrea Danese

Yu-wang Chen

Heppenstall Alison

Brendon Stubbs

Gordon Ward Fuller

Eleonora Iob

Andrew Prestwich

Thomas Sawczuk

Rebecca Hardy

Laurie Berrie

Liina Mansukoski

Johannes Textor

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