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Biography
Professor of Medical Statistics, NIHR Senior Investigator, Director of the Centre for Statistics in Medicine, Director of the UK EQUATOR Centre, University of Oxford.
Research interests primarily in diagnostic and prognostic prediction models (particularly validation) including methodological conduct and reporting, and using big data to develop and evaluate prediction models.
My research interests are primarily focused on methodological aspects surrounding the development and validation multivariable prediction (prognostic) models (design and analysis) and he has published extensively in this area. I am particularly interested in the sample size considerations and the role of big data in developing and evaluating prediction models. I am also interested in the systematic review and appraisal of prognostic studies and developed the CHARMS Checklist for conducting systematic reviews of prediction modelling studies.
Along with Doug Altman, Karel Moons and Hans Reitsma, UMC Utrecht, the Netherlands, I led an international collaboration to produce the TRIPOD consensus guidance on issues to report when developing or validating (prognostic and diagnostic) prediction models. This guidance is currently being extended for prediction models developed using 'big data' or individual participant data from multiple studies. Gary was involved in the development of PROBAST, a risk of bias tool to evaluate prediction model studies (using regression or machine learning/artificial intelligence methods). Two papers were published in the Annals of Internal Medicine describing the PROBAST checklist and and accompanying Explanation & Elaboration paper.
I am currently leading an international initiative to develop guidance for studies using artificial intelligence and machine learning (TRIPOD-AI). I was also been involved in other guidance for reporting artificial intelligence/machine learning studies including CONSORT-AI/SPIRIT-AI (for reporting AI intervention studies), STARD-AI (for reporting AI based diagnostic test accuracy [DTA] studies), and DECIDE-AI (bridging the development-implementation gap). I am also involved in developed risk of bias tools for machine learning diagnostic test accuracy studies (QUADAS-AI) and prediction model studies (PROBAST-AI). I am also involved with colleagues from the University of Southern California developming guidance for the responsible use of large language models such as ChatGPT for research (the CANGARU guidelines) and with colleagues from McMaster University for developing reporting guidance on studies evaluating chatbots for providing medical advice (CHART guideline).
I have been involved in the development of reporting guidelines including the GATHER statement for reporting global health estimates, and published in the Lancet and PLoS Medicine, and the AGReMA statement for reporting mediation analyses, published in JAMA. More recently I am involved in updating the SPIRIT and CONSORT guidelines. I am also a steering group member of the international STRATOS Initiative, which aims to provide accessible and accurate guidance in the design and analysis of observational studies, and in 2024 will be on the external advisory board for the Centre for Open Science Transparency and Openness Promotion Guidelines
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C49297/A27294