Improving prediction of linear regression models by integrating external information from heterogeneous populations: James-Stein estimators.
Biometrics
; 80(3)2024 Jul 01.
Article
em En
| MEDLINE
| ID: mdl-39101548
ABSTRACT
We consider the setting where (1) an internal study builds a linear regression model for prediction based on individual-level data, (2) some external studies have fitted similar linear regression models that use only subsets of the covariates and provide coefficient estimates for the reduced models without individual-level data, and (3) there is heterogeneity across these study populations. The goal is to integrate the external model summary information into fitting the internal model to improve prediction accuracy. We adapt the James-Stein shrinkage method to propose estimators that are no worse and are oftentimes better in the prediction mean squared error after information integration, regardless of the degree of study population heterogeneity. We conduct comprehensive simulation studies to investigate the numerical performance of the proposed estimators. We also apply the method to enhance a prediction model for patella bone lead level in terms of blood lead level and other covariates by integrating summary information from published literature.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Simulação por Computador
Limite:
Humans
Idioma:
En
Revista:
Biometrics
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos