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

TL;DR

This paper introduces a simple, robust g-computation method for hybrid control studies that remains valid even if the outcome regression model is misspecified, improving efficiency without assuming exchangeability.

Contribution

It proposes a model-robust g-computation approach that is easy to implement, consistent, asymptotically normal, and can leverage similarities between control groups.

Findings

Method is protected against outcome regression model misspecification.

Simulation study demonstrates improved efficiency and robustness.

Illustration with HIV trial data shows practical applicability.

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.