Professor Richard Riley BSc, MSc, PhD

Richard Riley

Department of Applied Health Sciences
Professor of Biostatistics

Contact details

Email
r.d.riley@bham.ac.uk
Twitter
@Richard_D_Riley
View my research portal
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’s YouTube channel is here and examples 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

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.

Menon, V, Shimelash, N, Rutunda, S, Nshimiyimana, C, Archer, L, Emmanuel-Fabula, M, Berhe, DF, Gill, J, Hezagira, E, Remera, E, Riley, R, Wong, R, Denniston, AK, Mateen, BA & Liu, X 2025, 'Assessing the potential utility of large language models for assisting community health workers: protocol for a prospective, observational study in Rwanda', BMJ open, vol. 15, no. 10, e110927. https://doi.org/10.1136/bmjopen-2025-110927

Pate, A, Sperrin, M, Riley, RD, Van Calster, B & Martin, GP 2025, 'calibmsm: An R package for calibration plots of the transition probabilities in a multistate model', PLOS ONE, vol. 20, no. 6, e0320504. https://doi.org/10.1371/journal.pone.0320504

Mozumder, SI, Booth, S, Riley, RD, Rutherford, MJ & Lambert, PC 2025, 'Calibration of cause-specific absolute risk for external validation using each cause-specific hazards model in the presence of competing events', Diagnostic and Prognostic Research, vol. 9, 23. https://doi.org/10.1186/s41512-025-00197-5

Lopez-Ayala, P, Riley, RD, Collins, GS & Zimmermann, T 2025, 'Dealing with continuous variables and modelling non-linear associations in healthcare data: practical guide', BMJ, vol. 390, e082440. https://doi.org/10.1136/bmj-2024-082440

Sekula, P, Steinbrenner, I, Schultheiss, UT, Valveny, N, Rebora, P, Halabi, S, Cadarette, SM, Riley, RD, Collins, GS, Sauerbrei, W, Gail, MH & Topic group 5 of the STRATOS initiative 2025, 'Design aspects for prognostic factor studies', BMJ open, vol. 15, no. 8, e095065. https://doi.org/10.1136/bmjopen-2024-095065

STRATIFY investigators, Wang, A, Koshiaris, C, Archer, L, Riley, RD, Snell, KIE, Stevens, R, Banerjee, A, Usher-Smith, JA, Swain, S, Clegg, A, Clark, CE, Payne, RA, Hobbs, RFD, Mcmanus, RJ & Sheppard, JP 2025, 'Developing prediction models for electrolyte abnormalities in patients indicated for antihypertensive therapy: evidence-based treatment and monitoring recommendations', Journal of Hypertension. https://doi.org/10.1097/HJH.0000000000004032

MuM-PreDiCT Group, Wambua, S, Crowe, FL, Thangaratinam, S, O'Reilly, D, McCowan, C, Brophy, S, Yau, C, Nirantharakumar, K, Riley, RD & Snell, KIE 2025, 'Development and validation of a postpartum cardiovascular disease risk prediction model in women incorporating reproductive and pregnancy-related predictors', BMC medicine, vol. 23, no. 1, 508. https://doi.org/10.1186/s12916-025-04229-1

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

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

Matos, J, Van Calster, B, Celi, LA, Dhiman, P, Gichoya, JW, Riley, RD, Russell, C, Khalid, S & Collins, GS 2025 'Critical appraisal of fairness metrics in clinical predictive AI' arXiv. https://doi.org/10.48550/arXiv.2506.17035

View all publications in research portal