A decomposition of Fisher’s information to inform sample size for developing or updating fair and precise clinical prediction models for individual risk—part 1: binary outcomes

A decomposition of Fisher’s information to inform sample size for developing or updating fair and precise clinical prediction models for individual risk—part 1: binary outcomes

Abstract Background When using a dataset to develop or update a clinical prediction model, small sample sizes increase concerns of overfitting, instability, poor predictive performance and a lack of fairness. For models estimating the risk of a binary outcome, previous research has outlined sample s...

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Bibliographic Details
Main Authors: Richard D. Riley, Gary S. Collins, Rebecca Whittle, Lucinda Archer, Kym I. E. Snell, Paula Dhiman, Laura Kirton, Amardeep Legha, Xiaoxuan Liu, Alastair K. Denniston, Frank E. Harrell, Laure Wynants, Glen P. Martin, Joie Ensor
Format: Article
Language:English
Published: BMC 2025-07-01
Series:Diagnostic and Prognostic Research
Subjects:
Clinical prediction models
Sample size
Uncertainty intervals
Instability
Classification
Fisher’s information matrix
Online Access:https://doi.org/10.1186/s41512-025-00193-9
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