A Model-Robust G-Computation Method for Analyzing Hybrid Control Studies Without Assuming Exchangeability

2026-02-02

AI Summary

This study addresses the bias introduced by external control data in hybrid controlled trials and the reliance of conventional methods on strong exchangeability assumptions. The authors propose a model-robust G-computation approach that achieves unbiased and efficient estimation under weaker assumptions by adjusting for baseline covariates. Notably, the method does not require exchangeability and retains consistency and asymptotic normality even when the outcome regression model is misspecified, offering robustness, simplicity, and efficiency. Integrating variable selection with covariate adjustment, theoretical analysis, simulation studies, and an empirical application to an HIV treatment trial demonstrate that the proposed method effectively controls bias and substantially improves estimation efficiency across diverse scenarios.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

There is growing interest in a hybrid control design for treatment evaluation, where a randomized controlled trial is augmented with external control data from a previous trial or a real world data source. The hybrid control design has the potential to improve efficiency but also carries the risk of introducing bias. The potential bias in a hybrid control study can be mitigated by adjusting for baseline covariates that are related to the control outcome. Existing methods that serve this purpose commonly assume that the internal and external control outcomes are exchangeable upon conditioning on a set of measured covariates. Possible violations of the exchangeability assumption can be addressed using a g-computation method with variable selection under a correctly specified outcome regression model. In this article, we note that a particular version of this g-computation method is protected against misspecification of the outcome regression model. This observation leads to a model-robust g-computation method that is remarkably simple and easy to implement, consistent and asymptotically normal under minimal assumptions, and able to improve efficiency by exploiting similarities between the internal and external control groups. The method is evaluated in a simulation study and illustrated using real data from HIV treatment trials.

Problem

• hybrid control design
• exchangeability
• g-computation
• outcome regression
• bias mitigation

Innovation

• model-robust
• g-computation
• hybrid control design
• exchangeability
• outcome regression