TY - JOUR
T1 - Uncertainty of risk estimates from clinical prediction models
T2 - rationale, challenges, and approaches
AU - Riley, Richard D
AU - Collins, Gary S
AU - Kirton, Laura
AU - Snell, Kym Ie
AU - Ensor, Joie
AU - Whittle, Rebecca
AU - Dhiman, Paula
AU - van Smeden, Maarten
AU - Liu, Xiaoxuan
AU - Alderman, Joseph
AU - Nirantharakumar, Krishnarajah
AU - Manson-Whitton, Jay
AU - Westwood, Andrew J
AU - Cazier, Jean-Baptiste
AU - Moons, Karel G M
AU - Martin, Glen P
AU - Sperrin, Matthew
AU - Denniston, Alastair K
AU - Harrell, Frank E
AU - Archer, Lucinda
PY - 2025/2/13
Y1 - 2025/2/13
N2 - 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.
AB - 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.
U2 - 10.1136/bmj-2024-080749
DO - 10.1136/bmj-2024-080749
M3 - Article
C2 - 39947680
SN - 0959-535X
VL - 388
JO - British Medical Journal
JF - British Medical Journal
M1 - e080749
ER -