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Debray, TPA, Collins, GS, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591, Van Calster, B, Reitsma, JB and Moons, KGM (2023) Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration. BMJ, 380. e071058 - ?.

Debray, TPA, Collins, GS, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591, Van Calster, B, Reitsma, JB and Moons, KGM (2023) Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist. BMJ, 380.

Pate, A, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Collins, GS, van Smeden, M, Van Calster, B, Ensor, J and Martin, GP (2023) Minimum sample size for developing a multivariable prediction model using multinomial logistic regression. Statistical Methods in Medical Research, 32 (3). pp. 555-571.

Dhiman, P, Ma, J, Andaur Navarro, CL, Speich, B, Bullock, G, Damen, JAA, Hooft, L, Kirtley, S, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Van Calster, B, Moons, KGM and Collins, GS (2022) Risk of bias of prognostic models developed using machine learning: a systematic review in oncology. Diagnostic and Prognostic Research, 6 (1). 13 - ?.

Dhiman, P, Ma, J, Andaur Navarro, CL, Speich, B, Bullock, G, Damen, JAA, Hooft, L, Kirtley, S, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Van Calster, B, Moons, KGM and Collins, GS (2022) Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review. BMC Medical Research Methodology, 22 (1). 101 - ?.

Dhiman, P, Ma, J, Navarro, CA, Speich, B, Bullock, G, Damen, JA, Kirtley, S, Hooft, L, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Van Calster, B, Moons, KGM and Collins, GS (2021) Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved. Journal of Clinical Epidemiology, 138. pp. 60-72.

Collins, GS, Dhiman, P, Andaur Navarro, CL, Ma, J, Hooft, L, Reitsma, JB, Logullo, P, Beam, AL, Peng, L, Van Calster, B, van Smeden, M, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 and Moons, KG (2021) Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open, 11 (7). e048008 - ?.

Van Calster, B, Wynants, L, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, van Smeden, M and Collins, GS (2021) Methodology over metrics: Current scientific standards are a disservice to patients and society. Journal of Clinical Epidemiology, 138. pp. 219-226.

Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Van Calster, B and Collins, GS (2020) A note on estimating the Cox-Snell R2 from a reported C statistic (AUROC) to inform sample size calculations for developing a prediction model with a binary outcome. Statistics in Medicine.

Wynants, L, Van Calster, B, Bonten, MMJ, Collins, GS, Debray, TPA, De Vos, M, Haller, MC, Heinze, G, Moons, KGM, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Schuit, E, Smits, LJM, Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591, Steyerberg, EW, Wallisch, C and van Smeden, M (2020) Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ, 369.

Wynants, L, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Timmerman, D and Van Calster, B (2018) Random-effects meta-analysis of the clinical utility of tests and prediction models. Statistics in Medicine, 37 (12). pp. 2034-2052.

Sundar, S, Rick, C, Dowling, F, Au, P, Snell, K, Rai, N, Champaneria, R, Stobart, H, Neal, R, Davenport, C, Mallett, S, Sutton, A, Kehoe, S, Timmerman, D, Bourne, T, Van Calster, B, Gentry-Maharaj, A, Menon, U, Deeks, J and ROCkeTS study group, A (2016) Refining Ovarian Cancer Test accuracy Scores (ROCkeTS): protocol for a prospective longitudinal test accuracy study to validate new risk scores in women with symptoms of suspected ovarian cancer. BMJ Open, 6 (8). e010333 - ?.