SynthesisBiostatistics (Oxford, England)2023
A meta-inference framework to integrate multiple external models into a current study.
Synthesis in Biostatistics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- ReFIT: Federated Transfer Learning for Sequential Prediction and Uncertainty Quantification Using Streaming EHR Data.Statistics in biosciences · 2026Article
- Mediation with External Summary Statistic Information.Biostatistics (Oxford, England) · 2025Article
- A zero-shot LLM framework for multimodal grievance classification, urgency scoring, and abuse detection in civic feedback systems.Scientific reports · 2025Article
- Robust angle-based transfer learning in high dimensions.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- Improving prediction of linear regression models by integrating external information from heterogeneous populations: James-Stein estimators.Biometrics · 2024Article
- Robust data integration from multiple external sources for generalized linear models with binary outcomes.Biometrics · 2024Article
- A synthetic data integration framework to leverage external summary-level information from heterogeneous populations.Biometrics · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
It is becoming increasingly common for researchers to consider incorporating external information from large studies to improve the accuracy of statistical inference instead of relying on a modestly sized data set collected internally. With some new predictors only available internally, we aim to build improved regression models based on individual-level data from an "internal" study while incorporating summary-level information from "external" models. We propose a meta-analysis framework along with two weighted estimators as the composite of empirical Bayes estimators, which combines the estimates from different external models. The proposed framework is flexible and robust in the ways that (i) it is capable of incorporating external models that use a slightly different set of covariates; (ii) it is able to identify the most relevant external information and diminish the influence of information that is less compatible with the internal data; and (iii) it nicely balances the bias-variance trade-off while preserving the most efficiency gain. The proposed estimators are more efficient than the naïve analysis of the internal data and other naïve combinations of external estimators.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.