Transportable inference using target population summary statistics under covariate shift

TL;DR

This paper introduces novel transportability methods that enable valid inference using source individual-level data and target population covariate summaries, addressing privacy constraints and covariate shift.

Contribution

It develops two new methods for transportability under covariate shift that require only covariate summaries from the target population, expanding applicability under data-sharing restrictions.

Findings

Entropy balancing effectively adjusts for covariate differences.

New flexible method accounts for covariate shift and summary uncertainty.

Methods are validated through simulations and real data analysis.

Abstract

Transporting findings from a study population to a target population is central to evidence-based decision-making in real-world settings. Most existing methods require individual-level data from both populations to account for covariate shift. However, privacy regulations and data-sharing constraints often preclude access to such data from the target population, leaving only covariate summaries available for analysis. In this paper, we develop transportability methods that enable valid inference using source individual-level data and target covariate summaries. Firstly, we apply entropy balancing to transportability, enabling source individual-level data to be adjusted to match the target covariate moments. We establish asymptotic normality for the entropy balancing estimator and propose a variance estimator to account for uncertainty in covariate summaries. Secondly, we develop a new transportability method that allows flexible modeling of covariate shift, thereby accounting for covariate shift and uncertainty in covariate summaries simultaneously. Asymptotic normality for the proposed estimator is established and its asymptotic variance is consistently estimated. The proposed method offers greater flexibility in accounting for covariate shift and thus permits consistent estimation and valid inference under weaker conditions than those required by entropy balancing. The proposed methods are evaluated by simulations and illustrated with an analysis of Surveillance, Epidemiology, and End Results breast cancer data.