Adaptive predictor-set linear model: An imputation-free method for linear regression prediction on data sets with missing values.
epigenetic aging clock
linear regression
missing values
predictive modeling
privacy
Journal
Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048
Informations de publication
Date de publication:
Jun 2024
Jun 2024
Historique:
revised:
25
03
2024
received:
24
03
2023
accepted:
01
04
2024
medline:
30
5
2024
pubmed:
30
5
2024
entrez:
30
5
2024
Statut:
ppublish
Résumé
Linear regression (LR) is vastly used in data analysis for continuous outcomes in biomedicine and epidemiology. Despite its popularity, LR is incompatible with missing data, which frequently occur in health sciences. For parameter estimation, this shortcoming is usually resolved by complete-case analysis or imputation. Both work-arounds, however, are inadequate for prediction, since they either fail to predict on incomplete records or ignore missingness-induced reduction in prediction accuracy and rely on (unrealistic) assumptions about the missing mechanism. Here, we derive adaptive predictor-set linear model (aps-lm), capable of making predictions for incomplete data without the need for imputation. It is derived by using a predictor-selection operation, the Moore-Penrose pseudoinverse, and the reduced QR decomposition. aps-lm is an LR generalization that inherently handles missing values. It is applied on a reference data set, where complete predictors and outcome are available, and yields a set of privacy-preserving parameters. In a second stage, these are shared for making predictions of the outcome on external data sets with missing entries for predictors without imputation. Moreover, aps-lm computes prediction errors that account for the pattern of missing values even under extreme missingness. We benchmark aps-lm in a simulation study. aps-lm showed greater prediction accuracy and reduced bias compared to popular imputation strategies under a wide range of scenarios including variation of sample size, goodness of fit, missing value type, and covariance structure. Finally, as a proof-of-principle, we apply aps-lm in the context of epigenetic aging clocks, linear models that predict a person's biological age from epigenetic data with promising clinical applications.
Identifiants
pubmed: 38813859
doi: 10.1002/bimj.202300090
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2300090Subventions
Organisme : Erasmus Universitair Medisch Centrum Rotterdam
Organisme : Deutsche Forschungsgemeinschaft
ID : FOR2488
Informations de copyright
© 2024 The Author(s). Biometrical Journal published by Wiley‐VCH GmbH.
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