A meta-inference framework to integrate multiple external models into a current study.

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;

SUBMITTER: Gu T 

PROVIDER: S-EPMC10102901 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature