Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

Abstract Background We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk fac...

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Détails bibliographiques
Auteurs principaux: Matthias Neumair, Michael W. Kattan, Stephen J. Freedland, Alexander Haese, Lourdes Guerrios-Rivera, Amanda M. De Hoedt, Michael A. Liss, Robin J. Leach, Stephen A. Boorjian, Matthew R. Cooperberg, Cedric Poyet, Karim Saba, Kathleen Herkommer, Valentin H. Meissner, Andrew J. Vickers, Donna P. Ankerst
Format: Article
Langue:English
Publié: BMC 2022-07-01
Collection:BMC Medical Research Methodology
Sujets:
Clinical risk prediction
Missing data
Prostate cancer
Validation
Accès en ligne:https://doi.org/10.1186/s12874-022-01674-x

Internet

https://doi.org/10.1186/s12874-022-01674-x

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