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The Royal Statistical Society Highlands Local Group would like to invite you to a talk Professor Richard Riley, Chair in Biostatistics, Applied Health Research, University of Birmingham, has previously given elsewhere but here will be a bit more technical.

Abstract:

Clinical prediction models estimate an individual’s risk of a particular health outcome. Well-known examples include QRISK for estimating 10-year CVD risk and the Nottingham Prognostic Index for estimating 5-year survival probability. Thousands of prediction models are published each year in the healthcare literature, yet very few are reliable or fit for purpose. In this talk, I suggest that prediction model research has become the academic’s playground, and that machine learning and AI have only exacerbated this problem. In particular, I discuss the findings of our living review of COVID-19 prediction models, and highlight common pitfalls such as small sample sizes, model instability, and no assessment of model calibration or clinical utility. I then focus on methods and initiatives to improve current standards, include new sample size calculations for model development and validation, and the TRIPOD reporting guidelines.

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Wynants L, Van Calster B, G. S. Collins, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020; 369: m1328

Riley RD, Collins GS. Stability of clinical prediction models developed using statistical or machine learning methods (Biometrical Journal, in-press)

Riley RD, Ensor J, Snell KIE, Harrell FE Jr, Martin GP, Reitsma JB, Moons KGM, Collins G, van Smeden M. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020;368:m441. doi: 10.1136/bmj.m441

Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement. Ann Intern Med 2015; 162: 55-63