Professor Richard Riley BSc, MSc, PhD

Department of Applied Health Sciences
Professor of Biostatistics

Contact details

Email: r.d.riley@bham.ac.uk
Twitter: @Richard_D_Riley

Address: Public Health Building
Department of Applied Health Sciences
University of Birmingham

Richard Riley is a Professor of Biostatistics at the University of Birmingham, leading a team of statisticians undertaking applied and methodology research for healthcare, especially in regard to prognosis, prediction models, and evidence synthesis.

Richard is Chief Statistics Editor for the BMJ and BMJ Medicine, a co-convenor of the Cochrane Prognosis Methods Group, Deputy Chair of the MRC-NIHR Better Methods Better Research panel, and a long-serving panel member of the NIHR Doctoral Fellowships scheme. He is the lead statistician on numerous applied and methodological healthcare related grants, from funders including the MRC and NIHR, and has published over 200 research articles and is lead Editor on 2 textbooks.

Richard hosts the websites www.prognosisresearch.com and www.ipdma.co.uk

ORCID ID: 0000-0001-8699-0735

Google Scholar Profile

Qualifications

PhD “Evidence Synthesis of Prognostic Marker Studies”, University of Leicester, 2006
MSc Medical Statistics, University of Leicester, 1999
BSc Mathematics with Statistics, University of Nottingham, 1998

Biography

Richard is a biostatistician with over twenty-years experience of methodology innovation and application. He is an expert on methods for meta-analysis, risk prediction and prognosis research. In meta-analysis, he specialises in methods for synthesising individual participant data (IPD), and is lead Editor of the book “Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research”, published by Wiley in 2021.

In prognosis, Richard co-leads the PROGRESS initiative (PROGnosis RESearch Strategy), that seeks to improve the standards of prognosis research, including the development and validation of risk prediction models. A major focus is his work on sample size calculations for developing and validation prediction models. He is the lead Editor of the book ‘Prognosis Research in Healthcare: Concepts, Methods and Impact’, published by Oxford University Press in 2019.

Richard is also a senior member of the steering committee for TRIPOD, which produce reporting guidelines for prediction model studies, and founder and co-convenor of the Cochrane Prognosis methods group, which produce guidance for undertaking systematic reviews of prognosis studies.

He leads various training courses in risk prediction, prognosis research and IPD meta-analysis at Keele, and hosts the websites www.prognosisresearch.com and www.ipdma.co.uk.

Richard has his own YouTube channel - some examples of his videos include:

Key Steps and Common Pitfalls in Clinical Prediction Model Research
Sample size calculations for clinical prediction model research (aka "goodbye rules of thumb"
Power Calculations for Individual Participant Data (IPD) Meta-Analysis Projects
Sample size calculations for external validation of a clinical prediction model
Stability of Clinical Prediction Models Developed Using Statistical or Machine Learning Approaches

Teaching

Richard leads various training courses in risk prediction, prognosis research and IPD meta-analysis at Keele, and details are shown at www.prognosisresearch.com and www.ipdma.co.uk.

Publications

Recent publications

Article

Tsegaye, B, Ma, J, Fryc, J, Yasmin, Z, Brookfield, M, Riley, RD, Dhiman, P & Collins, GS 2026, 'A critical review of clinical prediction models in oncology: is this SEERious research?', Journal of Clinical Epidemiology, pp. 112566. https://doi.org/10.1016/j.jclinepi.2026.112566

Whittle, R, Riley, RD, Archer, L, Collins, GS, Legha, A, Snell, KIE & Ensor, J 2026, 'A decomposition of Fisher’s information to inform sample size for developing or updating fair and precise clinical prediction models – part 3: continuous outcomes', Diagnostic and Prognostic Research, vol. 10, no. 1, 11. https://doi.org/10.1186/s41512-026-00228-9

Riley, RD, Whittle, R, Sadatsafavi, M, Martin, GP, Pate, A, Collins, GS & Ensor, J 2026, 'A general sample size framework for developing or updating a predictive algorithm: with application to clinical prediction models', BMC Medical Research Methodology. https://doi.org/10.1186/s12874-026-02856-7

Pate, A, Martin, GP & Riley, RD 2026, 'Agreement between heuristic shrinkage factor and optimal shrinkage factors in logistic regression for risk prediction: a simulation study across different sample sizes and settings', Diagnostic and Prognostic Research, vol. 10, no. 1, 15. https://doi.org/10.1186/s41512-026-00222-1

Henry, ML, O’Connell, NE, Riley, RD, Moons, KGM, Shea, BJ, Hooft, L, Wallwork, SB, Damen, JAAG, Skoetz, N, Appiah, RP, Berryman, C, Crouch, SM, Ferencz, GA, Grant, AR, Henry, KM, Herman, AM, Karran, EL, Koralegedera, I, Leake, HB, MacIntyre, E, Mouatt, B, Phuentsho, K, Van Der Laan, DA, Welsby, E, Wiles, LK, Wilkinson, EM, Wilson, MK, Wilson, MV & Moseley, GL 2026, 'Agreement testing of AMSTAR-PF, a tool for quality appraisal of systematic reviews of prognostic factor studies', BMJ Open, vol. 16, no. 1, e109388. https://doi.org/10.1136/bmjopen-2025-109388

Sadatsafavi, M, Gustafson, P, Setayeshgar, S, Wynants, L & Riley, RD 2026, 'Bayesian Sample Size Calculations for External Validation Studies of Risk Prediction Models', Statistics in Medicine, vol. 45, no. 3-5, e70389. https://doi.org/10.1002/sim.70389

Archer, L, Relton, SD, Akbari, A, Best, K, Bestwick, R, Bucknall, M, Conroy, S, Hattle, M, Hollinghurst, J, Howdon, D, Hulme, C, Humphrey, S, Lyons, RA, Nikolova, S, Rodriguez, MP, Richards, S, Walters, K, West, R, Van der Windt, D, Riley, RD & Clegg, A 2026, 'Development and evaluation of the electronic frailty index+ (eFI+) tool for older people: prognostic prediction modelling with integrated decision curve and health economic analysis', Health Technology Assessment, vol. 30, no. 61, pp. 1-154. https://doi.org/10.3310/GJAC1008

Allotey, J, Coomar, D, Ensor, J, Ruiz-Calvo, G, Boath, A, Ogwulu, CO, Monahan, M, Kabeya, V, Zheng, M, McNeill, R, Meacham, H, Mahmoud, G, Simpson, SA, Hitman, GA, Nirantharakumar, K, Heslehurst, N, Pelaez, M, Tonstad, S, Yeo, S, Cecatti, JG, Facchinetti, F, Motahari-Tabari, NS, Renault, KM, Guelfi, KJ, Jensen, DM, Harrison, C, Khomami, MB, Calle-Pascual, AL, McAuliffe, FM, Hauner, H, Barakat, R, Geiker, NRW, Vinter, CA, Phelan, S, Kinnunen, TI, Kothari, A, Teede, H, Poston, L, Betrán, AP, Moss, N, Iliodromiti, S, Austin, F, Roberts, T, Zamora, J, Riley, RD, Thangaratinam, S & i-WIP Collaborative Group 2026, 'Effects of lifestyle interventions in pregnancy on gestational diabetes: individual participant data and network meta-analysis', BMJ, vol. 392, e084159. https://doi.org/10.1136/bmj-2025-084159

Agweyu, A, Mwaniki, P, Menon, V, Korom, R, Isaaka, L, Wanyama, C, Gill, J, Kiptinness, S, Adan, N, Emmanuel-Fabula, M, Riley, R, Archer, L, Denniston, AK, Liu, X & Mateen, BA 2026, 'Generative AI-enabled clinical decision support system in primary care: a pragmatic, cluster-randomized trial', Nature Medicine, vol. 32, no. 8, pp. 3032–3039. https://doi.org/10.1038/s41591-026-04503-6

IAEA DLW Database Group 2026, 'Global external validation of fat mass prediction models for adults', Nature Health. https://doi.org/10.1038/s44360-026-00196-w

Preprint

Matos, J, Van Calster, B, Riley, RD, Dhiman, P & Collins, GS 2026 'Collapsibility of Performance Metrics in Clinical Predictive AI' arXiv. https://doi.org/10.48550/arXiv.2608.30568

Carriero, A, van Dam, MLG, de Hond, AAH, Riley, RD, Collins, GS, Moons, KGM, van Smeden, M & Erler, NS 2026 'Estimating Stability of Individual-Specific Predictions from AI-based prediction models using Bootstrap and Cross-validation Resampling' arXiv. https://doi.org/10.48550/arXiv.2610.33744

Martin, GP, Bladon, S, Whittle, R, Wells, M, Collins, GS & Riley, RD 2026 'Incorporating Missing Data Considerations into Sample Size Calculations for Developing Clinical Prediction Models' arXiv. https://doi.org/10.48550/arXiv.2605.07312

Review article

Allotey, J, Scandrett, K, Rochas, T, Coomar, D, Ruiz-Calvo, G, Miranda-Segura, A, Zheng, M, Mahmoud, G, Meacham, H, Abalos, E, Augustovski, F, Carroli, G, Cormick, G, Giordano, D, Gwako, G, Hofmeyr, GJ, Khan, K, Larbi, KK, Madliki, MS, McNeill, R, Mistry, H, Oladapo, OT, Pena-Rosas, JP, Smits, LJM, Snell, KIE, Thorson, A, Young, T, Qureshi, Z, Zamora, J, Riley, R, Vogel, JP, Betrán, AP & Thangaratinam, S 2026, 'Calcium supplementation for prevention of pre-eclampsia: a systematic review and individual participant data meta-analysis of randomised trials', The Lancet Obstetrics, Gynaecology and Women's Health, vol. 2, no. 10, pp. e895–e906. https://doi.org/10.1016/S3050-5038(26)00191-3

Matos, J, Van Calster, B, Celi, LA, Dhiman, P, Gichoya, JW, Riley, RD, Russell, C, Khalid, S & Collins, GS 2026, 'Critical appraisal of fairness metrics for artificial intelligence-based clinical prediction models: a scoping review', The Lancet. Digital health, pp. 101001. https://doi.org/10.1016/j.landig.2026.101001

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