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

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.

💡 Research Summary

This paper addresses a critical challenge in hybrid control designs, where a randomized controlled trial (RCT) is supplemented with external control data from prior studies or real‑world sources. While such designs can improve statistical efficiency and reduce cost, they risk bias if the internal and external control populations differ in ways not fully captured by measured covariates. Existing covariate‑adjustment methods (e.g., propensity‑score weighting, standard G‑computation) typically assume exchangeability of outcomes between internal and external controls conditional on a set of baseline covariates. Violations of this assumption can lead to biased treatment‑effect estimates.

The authors propose a model‑robust G‑computation method with variable selection (GC‑VS) that relaxes the exchangeability assumption and remains consistent even when the outcome regression model is misspecified. The method proceeds as follows. For the control arm (A = 0) they specify a generalized linear model (GLM) with a canonical link:

E(Y | A = 0, Z, X) = h