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.

翻译：混合对照设计在治疗评估中日益受到关注，该设计通过将随机对照试验与既往试验或真实世界数据源的外部对照数据相结合。混合对照设计有望提高效率，但也存在引入偏倚的风险。通过调整与对照结局相关的基线协变量，可降低混合对照研究中潜在的偏倚风险。现有方法通常假设在调整一组测量协变量后，内部与外部对照结局具有可交换性。对于可交换性假设可能违背的情况，可在正确指定的结局回归模型下通过变量选择的G-计算方法加以解决。本文发现该G-计算方法的一个特例对结局回归模型的错误设定具有鲁棒性。这一发现催生了一种模型鲁棒G-计算方法，该方法极其简单易行，在最小假设下具有一致性和渐近正态性，并能通过利用内部与外部对照组的相似性提高效率。通过模拟研究对该方法进行评价，并利用HIV治疗试验的真实数据加以验证。