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Lookup NU author(s): Matt McTeer, Professor Quentin AnsteeORCiD, Professor Paolo MissierORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 by the authors.Aims: Overlapping asymmetric data sets are where a large cohort of observations have a small amount of information recorded, and within this group there exists a smaller cohort which have extensive further information available. Missing imputation is unwise if cohort size differs substantially; therefore, we aim to develop a way of modelling the smaller cohort whilst considering the larger. Methods: Through considering traditionally once penalized P-Spline approximations, we create a second penalty term through observing discrepancies in the marginal value of covariates that exist in both cohorts. Our now twice penalized P-Spline is designed to firstly prevent over/under-fitting of the smaller cohort and secondly to consider the larger cohort. Results: Through a series of data simulations, penalty parameter tunings, and model adaptations, our twice penalized model offers up to a 58% and 46% improvement in model fit upon a continuous and binary response, respectively, against existing B-Spline and once penalized P-Spline methods. Applying our model to an individual’s risk of developing steatohepatitis, we report an over 65% improvement over existing methods. Conclusions: We propose a twice penalized P-Spline method which can vastly improve the model fit of overlapping asymmetric data sets upon a common predictive endpoint, without the need for missing data imputation.
Author(s): McTeer M, Henderson R, Anstee QM, Missier P
Publication type: Article
Publication status: Published
Journal: Mathematics
Year: 2024
Volume: 12
Issue: 5
Online publication date: 05/03/2024
Acceptance date: 03/03/2024
Date deposited: 26/03/2024
ISSN (electronic): 2227-7390
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
URL: https://doi.org/10.3390/math12050777
DOI: 10.3390/math12050777
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