Professor Richard Riley BSc, MSc, PhD

Richard Riley

Institute of Applied Health Research
Professor of Biostatistics

Contact details

Email
r.d.riley@bham.ac.uk
Twitter
@Richard_D_Riley
View my research portal
Address
Public Health Building
Institute of Applied Health Research
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

MuM-PreDiCT Group 2024, 'Association between pregnancy-related complications and development of type 2 diabetes and hypertension in women: an umbrella review', BMC medicine, vol. 22, no. 1, 66. https://doi.org/10.1186/s12916-024-03284-4

Whittle, R, Ensor, J, Hattle, M, Dhiman, P, Collins, GS & Riley, RD 2024, 'Calculating the power of a planned individual participant data meta-analysis to examine prognostic factor effects for a binary outcome', Research Synthesis Methods. https://doi.org/10.1002/jrsm.1737

Pate, A, Sperrin, M, Riley, RD, Peek, N, Van Staa, T, Sergeant, JC, Mamas, MA, Lip, GYH, O'Flaherty, M, Barrowman, M, Buchan, I & Martin, GP 2024, 'Calibration plots for multistate risk predictions models', Statistics in Medicine. https://doi.org/10.1002/sim.10094

Archer, L, Hattle, M, Riley, RD & The eFI+ investigators 2024, 'Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults', Age and Ageing, vol. 53, no. 3, afae057. https://doi.org/10.1093/ageing/afae057

for the International Prediction of Pregnancy Complications collaborative network 2024, 'Development and validation of a prognostic model to predict birth weight: individual participant data meta-analysis', BMJ Medicine, vol. 3, no. 1, e000784. https://doi.org/10.1136/bmjmed-2023-000784

Collins, GS, Dhiman, P, Ma, J, Schlussel, MM, Archer, L, Van Calster, B, Jr, FEH, Martin, GP, Moons, KGM, van Smeden, M, Sperrin, M, Bullock, GS & Riley, RD 2024, 'Evaluation of clinical prediction models (part 1): from development to external validation', BMJ, vol. 384, e074819. https://doi.org/10.1136/bmj-2023-074819

Riley, RD, Archer, L, Snell, KIE, Ensor, J, Dhiman, P, Martin, GP, Bonnett, LJ & Collins, GS 2024, 'Evaluation of clinical prediction models (part 2): how to undertake an external validation study', BMJ (Clinical research ed.), vol. 384, e074820. https://doi.org/10.1136/bmj-2023-074820

Riley, RD, Snell, KIE, Archer, L, Ensor, J, Debray, TPA, Van Calster, B, Van Smeden, M & Collins, GS 2024, 'Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study', BMJ, vol. 384, e074821. https://doi.org/10.1136/bmj-2023-074821

Hattle, M, Ensor, J, Scandrett, K, van Middelkoop, M, van der Windt, DA, Holden, MA & Riley, RD 2024, 'Individual participant data meta‐analysis to examine linear or non‐linear treatment‐covariate interactions at multiple time‐points for a continuous outcome', Research Synthesis Methods. https://doi.org/10.1002/jrsm.1750

Monitoring Inflammatory Conditions Investigators, Leaviss, J, Carroll, C, Essat, M, van der Windt, D, Grainge, MJ, Card, T, Riley, R & Abhishek, A 2024, 'Prognostic factors for liver, blood and kidney adverse events from glucocorticoid sparing immune-suppressing drugs in immune-mediated inflammatory diseases: a prognostic systematic review', RMD Open, vol. 10, no. 1, e003588. https://doi.org/10.1136/rmdopen-2023-003588

Abhishek, A, Grainge, M, Card, T, Williams, HC, Taal, MW, Aithal, GP, Fox, CP, Mallen, CD, Stevenson, MD, Nakafero, G & Riley, R 2024, 'Risk-stratified monitoring for sulfasalazine toxicity: prognostic model development and validation', RMD Open, vol. 10, no. 1, e003980. https://doi.org/10.1136/rmdopen-2023-003980

Andaur Navarro, CL, Damen, JAA, Ghannad, M, Dhiman, P, van Smeden, M, Reitsma, JB, Collins, GS, Riley, RD, Moons, KGM & Hooft, L 2024, 'SPIN-PM: A consensus framework to evaluate the presence of spin in studies on prediction models', Journal of Clinical Epidemiology. https://doi.org/10.1016/j.jclinepi.2024.111364

Riley, RD, Collins, GS, Hattle, M, Whittle, R & Ensor, J 2023, 'Calculating the power of a planned individual participant data meta‐analysis of randomised trials to examine a treatment‐covariate interaction with a time‐to‐event outcome', Research Synthesis Methods. https://doi.org/10.1002/jrsm.1650

Review article

Crocker, TF, Ensor, J, Lam, N, Jordão, M, Bajpai, R, Bond, M, Forster, A, Riley, RD, Andre, D, Brundle, C, Ellwood, A, Green, J, Hale, M, Mirza, L, Morgan, J, Patel, I, Patetsini, E, Prescott, M, Ramiz, R, Todd, O, Walford, R, Gladman, J & Clegg, A 2024, 'Community based complex interventions to sustain independence in older people: systematic review and network meta-analysis', BMJ, vol. 384, no. 8422, e077764. https://doi.org/10.1136/bmj-2023-077764

Levis, B, Snell, KIE, Damen, JAA, Hattle, M, Ensor, J, Dhiman, P, Andaur Navarro, CL, Takwoingi, Y, Whiting, PF, Debray, TPA, Reitsma, JB, Moons, KGM, Collins, GS & Riley, RD 2024, 'Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed', Journal of Clinical Epidemiology, vol. 165, 111206. https://doi.org/10.1016/j.jclinepi.2023.10.022

View all publications in research portal