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Prediction models and prognosis research

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We specialise in developing and applying robust methodology for prognosis research and prediction models in healthcare. Our methods address the whole research pipeline from initial study design and model training, to validation, reporting, implementation and impact assessment, including systematic reviews and meta-analysis. We educate researchers worldwide through online CPD courses, good-practice guidelines, software packages and textbooks.

Research theme leads

  • Professor Richard Riley

    Professor of Biostatistics

    Staff profile for Richard Riley, Professor of Biostatistics, Department of Applied Health Sciences, University of Birmingham

  • Professor Gary S. Collins

    125th Anniversary Chair

    Staff profile for Professor Gary S. Collins, 125th Anniversary Chair and Professor of Medical Statistics within the Department of Applied Health Sciences, College of Medicine and Health, University of Birmingham.

  • Dr Joie Ensor

    Associate Professor in Biostatistics

    Staff profile page for Dr Joie Ensor, Associate Professor in Biostatics within the Department of Applied Health Sciences, College of Medicine and Health, University of Birmingham.

  • Dr Kym Snell

    Associate Professor in Biostatistics

    Staff profile for Dr Kym Snell, Associate Professor in Biostatistics in the Department of Applied Health Sciences at the University of Birmingham

The Prediction Models Group are part of BESTEAM.

BESTEAM members

About the research

Our Key aims are to:

  • To undertake methodology research to improve prediction models in healthcare, from development to evaluation, implementation and impact
  • To support applied research in clinical prediction models, both locally, nationally and internationally
  • To undertake applied and methodology research in prognosis (i.e., overall prognosis, prognostic factors, prognostic models, predictors of treatment effect), following the PROGRESS framework
  • To improve the transparency and completeness of reporting in prognosis and prediction model research, by leading TRIPOD guidelines and extensions
  • To undertake and produce standards for Systematic Reviews of Prognosis Research and Prediction Models, including co-leading the Cochrane Prognosis Methods Group
  • To educate, train and share best practice for undertaking prognosis studies and clinical prediction model research

Projects

  • Sample size calculations for prediction model development, updating and model evaluation – funded by EPSRC and NIHR-MRC
  • Sample size calculations for IPD meta-analysis projects (funded by NIHR-MRC)
  • Systematic reviews and meta-analysis of prognosis research and prediction studies (including Cochrane Handbook) (funded by NIHR-MRC)
  • Advanced meta-analysis methods, including IPD meta-analysis and multivariate meta-analysis methods for prognosis and prediction
  • Regulation, implementation and fairness of prediction models
  • Software (Stata, R, Python) for prediction and meta-analysis methods
  • Critical appraisal tools and reporting guidelines, including TRIPOD
  • PPIE involvement in methods projects
  • Applied research – our team collaborate on many healthcare research projects for prognosis and prediction models, both locally, nationally and internationally

Workshops, conferences and seminars

For more details see the Prognosis and Research courses and events page andthe IPDMA training courses page.

Key publications

  • Legha A, Ensor J, Whittle R, Archer L, et al. Sequential sample size calculations and learning curves safeguard the robust development of a clinical prediction model for individuals. J Clin Epidemiol. 2026 Mar;191:112117. doi: 10.1016/j.jclinepi.2025.112117
  • Whittle R, Ensor J, Archer L, et al. Extended sample size calculations for evaluation of prediction models using a threshold for classification. BMC Med Res Methodol. 2025 Jul 1;25(1):170. doi: 10.1186/s12874-025-02592-4. PMID: 40596983; PMCID: PMC12210805
  • Archer L, Relton SD, Akbari A, et al. 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 Ageing. 2024 Mar 1;53(3):afae057. doi: 10.1093/ageing/afae057
  • Ensor J (2023). pmsampsize: Sample Size for Development of a Prediction Model. CRAN Package PageRiley RD, van der Windt D, Croft P, Moons KGM, editors. Prognosis Research in Healthcare: Concepts, Methods and Impact. Oxford, UK: Oxford University Press; 2019
  • Riley RD, Tierney J, Stewart LA (Eds). Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research. Wiley, Chichester; 2021
  • Moons KGM, Riley RD, editors. Cochrane Handbook for Systematic Reviews of Prognosis Research and Prediction Models. Wiley (2026)
  • Collins G S, Moons K G M, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods BMJ 2024; 385 :e078378
  • Snell KIE, Levis B, Damen JAA, et al. Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA). BMJ. 2023;381:e073538
  • Riley RD, Ensor J, Snell KIE, Harrell FE, Jr., Martin GP, Reitsma JB, et al. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020;368:m441.
  • Collins GS, Dhiman P, Ma J, et al. Evaluation of clinical prediction models (part 1): from development to external validation. BMJ. 2024;384:e074819
  • Riley RD, Archer L, Snell KIE, et al. Evaluation of clinical prediction models (part 2): how to undertake an external validation study. BMJ. 2024;384:e074820
  • Riley RD, Snell KIE, Archer L, et al. Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study. BMJ. 2024;384:e074821
  • Legha A, Ensor J, Whittle R, Archer L, et al. Sequential sample size calculations and learning curves safeguard the robust development of a clinical prediction model for individuals. J Clin Epidemiol. 2026 Mar;191:112117. doi: 10.1016/j.jclinepi.2025.112117
  • Whittle R, Ensor J, Archer L, et al. Extended sample size calculations for evaluation of prediction models using a threshold for classification. BMC Med Res Methodol. 2025 Jul 1;25(1):170. doi: 10.1186/s12874-025-02592-4. PMID: 40596983; PMCID: PMC12210805
  • Archer L, Relton SD, Akbari A, et al. 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 Ageing. 2024 Mar 1;53(3):afae057. doi: 10.1093/ageing/afae057
  • Ensor J (2023). pmsampsize: Sample Size for Development of a Prediction Model. CRAN Package Page

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