ArticleBiometrics2024
Improving prediction of linear regression models by integrating external information from heterogeneous populations: James-Stein estimators.
Article in Biometrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- A new integrative learning framework for integrating multiple secondary outcomes into primary outcome analysis: a case study on liver health.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2026Article
- ReFIT: Federated Transfer Learning for Sequential Prediction and Uncertainty Quantification Using Streaming EHR Data.Statistics in biosciences · 2026Article
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Authors and funding
5 authors.
Funding
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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Registered trials
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