Professor Gary S. Collins PhD

Professor Gary S. Collins

Department of Applied Health Sciences
125th Anniversary Chair
Professor of Medical Statistics

Contact details

Email
g.s.collins@bham.ac.uk
View my research portal
Address
Department of Applied Health Sciences
Public Health Building
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Professor Gary Collins is a 125th Anniversary Chair and Professor of Medical Statistics. He is a NIHR Senior Investigator. His research interests are primarily focused on methodological aspects in the development and fair evaluation of clinical prediction models (including machine learning) and has published extensively in this area. His other research interests lie in improving the transparency, openness and quality of health research, where he leads the EQUATOR Network activities in the UK.

He is the founding Editor-in-Chief of the journal Diagnostic and Prognostic Research, and a statistics editor for the BMJ.

You can follow Gary on LinkedIn, BlueSky, X, GitHub, and ORCID

Google Scholar Profile

Qualifications

  • PhD in Mathematical Statistics, University of Exeter, 2000
  • BSc (Hons) in Mathematical Methods for Information Technology, Nottingham Trent University, 1996

Biography

Gary is a statistician with over 25 years experience in both methodology and applied research. He is an expert in the development and validation of clinical prediction models/predictive AI tools. Gary’s current major research activity focusses on their evaluation, with a particular interest on fairness considerations.

Before joining the University of Birmingham, he was at the University of Oxford between 2006 and 2025 working alongside Professor Doug Altman. At Oxford he was the Director of the Centre for Statistics in Medicine and made a Professor of Medical Statistics in 2016. During this period, he also became interested in tackling how to improve the quality and transparency of health research, evaluating published research and driving/supporting initiatives to improve how research is designed, conducted and reported. In recognition of this strand of his research, in 2018, he took on the directorship of the EQUATOR Network activities in the UK, which he still leads.

Gary is a steering group member, and former co-chair of the international STRATOS Initiative, which aims to provide accessible and accurate guidance in the design and analysis of observational studies, and currently sits on the external advisory board for the Centre for Open Science Transparency and Openness Promotion Guidelines. He is also on the advisory board of the LATITUDES Network. Gary has had multiple involvements with NIHR, including being a member of the NIHR HTA Commissioning Board from 2016 to 2020, and currently on the NIHR doctoral research fellowship committee. He has been an NIHR Senior investigator since 2024.

Postgraduate supervision

Gary is interested in supervising doctoral research students in the following areas:

  • methodology for clinical prediction models
  • predictive AI/machine learning for healthcare
  • methodology for AI evaluation and fairness
  • reporting standards and guidelines
  • meta-science and open science methods and evaluation
  • research transparency

Research

Gary works on numerous international research initiatives to improve the methodology, and develop standards and frameworks for research involving predictive AI/clinical prediction models for healthcare. Improving the quality and transparency of predictive AI research drives his research, so that the tools used in practice are robust, safe and improve clinical workflows.

Gary leads the international TRIPOD consortium which he established alongside Carl Moons, Hans Reitsma and Doug Altman. He led the initiative to develop the TRIPOD statement; a set reporting recommendations for studies developing and validating clinical prediction models, which were published in eleven leading medical journals in 2015. He led an update to these recommendations which were published in 2024 to cover models developed using artificial intelligence and machine learning methods (TRIPOD+AI) and large language models (TRIPOD-LLM).

Gary is also involved in other guidance for artificial intelligence and machine learning studies in healthcare, including

  • STARD-AI - for AI based diagnostic test accuracy studies
  • DECIDE-AI - for early-stage live clinical evaluation of AI systems
  • CONSORT-AI / SPIRIT-AI - for AI intervention studies
  • CHART - for evaluating chatbots providing medical advice 

Gary is involved in development of risk of bias tools for machine learning diagnostic test accuracy studies (QUADAS-AI) and prediction model studies (PROBAST+AI), and the FUTURE-AI principles for trustworthy and deployable artificial intelligence in healthcare.

Gary is involved in the development of numerous other reporting guidelines for other study designs, including the

Publications

Recent publications

Article

Valsamis, EM, Beck Larsen, J, Thillemann, TM, Gwilym, SE, Collins, GS, Mechlenburg, I & Rees, JL 2025, 'A comparative study of shoulder replacement outcomes using linked national registry and hospital data from England and Denmark', BMC medicine, vol. 23, no. 1, 180. https://doi.org/10.1186/s12916-025-04003-3

Riley, RD, Collins, GS, Whittle, R, Archer, L, Snell, KIE, Dhiman, P, Kirton, L, Legha, A, Liu, X, Denniston, AK, Harrell, FE, Wynants, L, Martin, GP & Ensor, J 2025, 'A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models for individual risk-part 1: binary outcomes', Diagnostic and Prognostic Research, vol. 9, no. 1, 14. https://doi.org/10.1186/s41512-025-00193-9

Riley, R, Collins, G, Archer, L, Whittle, R, Legha, A, Kirton, L, Dhiman, P, Sadatsafavi, M, Adderley, N, Alderman, J, Martin, GP & Ensor, J 2025, 'A decomposition of Fisher’s information to inform sample size for developing or updating fair and precise clinical prediction models - Part 2: time-to-event outcomes', Diagnostic and Prognostic Research.

Lawrence, NR, Bacila, I, Tonge, J, Dawson, J, Collins, GS, Lang, ZQ, Bryce, J, Alimussina, M, Chen, M, Ali, SR, Adam, S, Van Den Akker, ELT, Bachega, TASS, Baronio, F, Birkebæk, NH, Bonfig, W, Claahsen - Van Der Grinten, H, Cools, M, Costa, EC, Debono, M, De Vries, L, Flück, CE, Gazdagh, G, Güven, A, Hannema, SE, Iotova, V, Van Der Kamp, HJ, Krone, R, Leka-Emiri, S, Clemente-León, M, Lichiardopol, CR, Markosyan, RL, Milenkovic, T, De Miranda, MC, Neumann, U, Newell-Price, J, Poyrazoǧlu, Ş, Probst-Scheidegger, U, Russo, G, De Sanctis, L, Seneviratne, SN, Stancampiano, MR, Tadokoro-Cuccaro, R, Thankamony, A, Vieites, A, Wasniewska, M, Yeste, D, Tomlinson, J, Ahmed, SF & Krone, N 2025, 'Blood pressure and its associations in 554 children and young people with congenital adrenal hyperplasia', European Journal of Endocrinology , vol. 192, no. 5, pp. 529-539. https://doi.org/10.1093/ejendo/lvaf060

Safiri, S, Grieger, JA, Ghaffari Jolfayi, A, Mousavi, SE, Nejadghaderi, SA, Fazlollahi, A, Sullman, MJM, Karamzad, N, Sahin, F, Singh, K, Collins, GS & Kolahi, AA 2025, 'Burden of diseases attributable to excess body weight in 204 countries and territories, 1990–2019', Nutrition Journal, vol. 24, no. 1, 23. https://doi.org/10.1186/s12937-025-01082-z

Truchot, A, Raynaud, M, Helanterä, I, Aubert, O, Kamar, N, Divard, G, Astor, B, Legendre, C, Hertig, A, Buchler, M, Crespo, M, Akalin, E, Pujol, GS, Ribeiro De Castro, MC, Matas, AJ, Ulloa, C, Jordan, SC, Huang, E, Juric, I, Basic-Jukic, N, Coemans, M, Naesens, M, Friedewald, JJ, Silva, HT, Lefaucheur, C, Segev, DL, Collins, GS & Loupy, A 2025, 'Competing and Noncompeting Risk Models for Predicting Kidney Allograft Failure', Journal of the American Society of Nephrology, vol. 36, no. 4, pp. 688-701. https://doi.org/10.1681/ASN.0000000517

Hopewell, S, Chan, A-W, Collins, GS, Hróbjartsson, A, Moher, D, Schulz, KF, Tunn, R, Aggarwal, R, Berkwits, M, Berlin, JA, Bhandari, N, Butcher, NJ, Campbell, MK, Chidebe, RCW, Elbourne, D, Farmer, A, Fergusson, DA, Golub, RM, Goodman, SN, Hoffmann, TC, Ioannidis, JPA, Kahan, BC, Knowles, RL, Lamb, SE, Lewis, S, Loder, E, Offringa, M, Ravaud, P, Richards, DP, Rockhold, FW, Schriger, DL, Siegfried, NL, Staniszewska, S, Taylor, RS, Thabane, L, Torgerson, D, Vohra, S, White, IR & Boutron, I 2025, 'CONSORT 2025 explanation and elaboration: updated guideline for reporting randomised trials', BMJ (Clinical research ed.), vol. 389, e081124. https://doi.org/10.1136/bmj-2024-081124

Hopewell, S, Chan, A-W, Collins, GS, Hróbjartsson, A, Moher, D, Schulz, KF, Tunn, R, Aggarwal, R, Berkwits, M, Berlin, JA, Bhandari, N, Butcher, NJ, Campbell, MK, Chidebe, RCW, Elbourne, D, Farmer, A, Fergusson, DA, Golub, RM, Goodman, SN, Hoffmann, TC, Ioannidis, JPA, Kahan, BC, Knowles, RL, Lamb, SE, Lewis, S, Loder, E, Offringa, M, Ravaud, P, Richards, DP, Rockhold, FW, Schriger, DL, Siegried, NL, Staniszewska, S, Taylor, RS, Thabane, L, Torgerson, D, Vohra, S, White, IR & Boutron, I 2025, 'CONSORT 2025 statement: updated guideline for reporting randomized trials: a Korean translation', Ewha medical journal, vol. 48, no. 3, e50. https://doi.org/10.12771/emj.2025.00409

Preprint

Riley, RD, Collins, GS, Whittle, R, Archer, L, Snell, KIE, Dhiman, P, Kirton, L, Legha, A, Liu, X, Denniston, A, Harrell Jr, FE, Wynants, L, Martin, GP & Ensor, J 2025 'A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- part 1: binary outcomes' arXiv. https://doi.org/10.48550/arXiv.2407.09293

Riley, RD, Collins, GS, Archer, L, Whittle, R, Legha, A, Kirton, L, Dhiman, P, Sadatsafavi, M, Adderley, NJ, Alderman, J, Martin, GP & Ensor, J 2025 'A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- Part 2: time-to-event outcomes' arXiv. <https://arxiv.org/abs/2501.14482>

Whittle, R, Riley, RD, Archer, L, Collins, GS, Legha, A, Snell, KIE & Ensor, J 2025 'A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models -- Part 3: continuous outcomes' arXiv. https://doi.org/10.48550/arXiv.2507.2354

Riley, RD, Whittle, R, Sadatsafavi, M, Martin, GP, Pate, A, Collins, GS & Ensor, J 2025 'A general sample size framework for developing or updating a clinical prediction model' arXiv. https://doi.org/10.48550/arXiv.2504.18730

Shiferaw, KB, Balaur, I, Collins, G, Sharma, C, Castro, LJ, Psomopoulos, F, Garijo, D, Henkel, R, Waltemath, D & Zeleke, AA 2025 'Calibrating CONSORT-AI with FAIR Principles to enhance reproducibility in AI-driven clinical trials' medRxiv. https://doi.org/10.1101/2025.07.07.25330987

Hughes-Noehrer, L, Parkes, MJ, Stewart, A, Wilson, AJ, Collins, GS, Riley, RD, Mathur, M, Fox, MP, Islam, N, Zivich, PN & Feeney, TJ 2025 'Code Sharing in Healthcare Research: A Practical Guide and Recommendations for Good Practice' arXiv. https://doi.org/10.48550/arXiv.2510.19279

Review article

Collins, GS, Chester-Jones, M, Gerry, S, Ma, J, Sehjal, J, Matos, J, Tsegaye, B & Dhiman, P 2025, 'Clinical prediction models using machine learning in oncology: challenges and recommendations', BMJ Oncology, vol. 4, no. 1, e000914. https://doi.org/10.1136/bmjonc-2025-000914

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