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

TL;DR

This paper introduces a flexible meta-inference framework that effectively integrates external summary data into internal regression models, enhancing accuracy while managing differences in external models and covariates.

Contribution

It proposes a novel meta-analysis framework with weighted estimators that adaptively combine external models, improving inference efficiency over naive methods.

Findings

The framework effectively incorporates external models with different covariates.

It identifies and down-weights less compatible external information.

The estimators outperform naive internal-only analyses in efficiency.

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 dataset 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 the 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 can 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 naive analysis of the internal data and other naive combinations of external estimators.