Multi-cohort modeling strategies for scalable globally accessible prostate cancer risk tools

Multi-cohort modeling strategies for scalable globally accessible prostate cancer risk tools

Abstract Background Online clinical risk prediction tools built on data from multiple cohorts are increasingly being utilized for contemporary doctor-patient decision-making and validation. This report outlines a comprehensive data science strategy for building such tools with application to the Pro...

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Bibliographic Details
Main Authors: Johanna Tolksdorf, Michael W. Kattan, Stephen A. Boorjian, Stephen J. Freedland, Karim Saba, Cedric Poyet, Lourdes Guerrios, Amanda De Hoedt, Michael A. Liss, Robin J. Leach, Javier Hernandez, Emily Vertosick, Andrew J. Vickers, Donna P. Ankerst
Format: Article
Language:English
Published: BMC 2019-10-01
Series:BMC Medical Research Methodology
Subjects:
Risk prediction
Prostate cancer
Validation
Calibration
Discrimination
Net benefit
Online Access:http://link.springer.com/article/10.1186/s12874-019-0839-0

Internet

http://link.springer.com/article/10.1186/s12874-019-0839-0

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