Uncertainty of risk estimates from clinical prediction models: rationale, challenges, and approaches
Richard D Riley, Gary S. Collins, Laura Kirton, Kym I E Snell, Joie Ensor, Rebecca Whittle, Paula Dhiman, Maarten van Smeden, Xiaoxuan Liu, Joseph Alderman, Krishnarajah Nirantharakumar, Jay Manson-Whitton, Andrew J. Westwood, Jean‐Baptiste Cazier, Karel G. M. Moons, Glen P. Martin, Matthew Sperrin, Alastair K. Denniston, Frank E. Harrell, Lucinda Archer
Abstract
Clinical prediction models estimate an individual’s risk (probability) of a health related outcome to help guide patient counselling and clinical decision making. Most models provide a single point estimate of risk but without the associated uncertainty. Riley and colleagues argue that this needs to change, as understanding uncertainty of risk estimates helps to inform critical evaluation of a model and may impact shared decision making. Examples are provided to illustrate uncertainty in risk estimates, and key methods to quantify and present uncertainty are discussed.