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：在治疗评估领域，混合控制设计日益受到关注，该设计通过将随机对照试验与来自既往试验或真实世界数据源的外部对照数据进行整合来增强研究效力。混合控制设计虽具备提升效率的潜力，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，可有效缓解混合控制研究中潜在的偏倚。现有服务于该目的的方法通常假设在给定一组已测量协变量的条件下，内部与外部对照结局具有可交换性。针对可交换性假设可能被违背的情况，可在正确设定的结局回归模型下采用结合变量选择的G-计算方法进行处理。本文指出，该G-计算方法的一个特定版本对结局回归模型的误设具有稳健性。这一发现催生了一种模型稳健的G-计算方法，其具有显著简洁性且易于实施，在最小假设下保持一致性及渐近正态性，并能通过利用内部与外部对照组间的相似性提升估计效率。本方法通过模拟研究进行评估，并利用HIV治疗试验的真实数据加以演示。