Comparing conventional and Bayesian workflows for clinical outcome prediction modelling with an exemplar cohort study of severe COVID-19 infection incorporating clinical biomarker test results

Comparing conventional and Bayesian workflows for clinical outcome prediction modelling with an exemplar cohort study of severe COVID-19 infection incorporating clinical biomarker test results

Abstract Purpose Assessing risk factors and creating prediction models from real-world medical data is challenging, requiring numerous modelling decisions with clinical guidance. Logistic regression is a common model for such studies, for which we advocate the use of Bayesian methods that can jointl...

Full description

Saved in:
Bibliographic Details
Main Authors: Brian Sullivan, Edward Barker, Louis MacGregor, Leo Gorman, Philip Williams, Ranjeet Bhamber, Matt Thomas, Stefan Gurney, Catherine Hyams, Alastair Whiteway, Jennifer A. Cooper, Chris McWilliams, Katy Turner, Andrew W. Dowsey, Mahableshwar Albur
Format: Article
Language:English
Published: BMC 2025-03-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Projective prediction
Bayesian
Logistic regression
Risk factors
COVID-19
Online Access:https://doi.org/10.1186/s12911-025-02955-3
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1186/s12911-025-02955-3

Similar Items