Externally controlled single-arm trials are critical to assess treatment efficacy across therapeutic indications for which randomized controlled trials are not feasible. A closely-related research design, the unanchored indirect treatment comparison, is often required for disconnected treatment networks in health technology assessment. We present a unified causal inference framework for both research designs. We develop a novel estimator that augments a popular weighting approach based on entropy balancing -- matching-adjusted indirect comparison (MAIC) -- by fitting a model for the conditional outcome expectation. The predictions of the outcome model are combined with the entropy balancing MAIC weights. While the standard MAIC estimator is singly robust where the outcome model is non-linear, our augmented MAIC approach is doubly robust, providing increased robustness against model misspecification. This is demonstrated in a simulation study with binary outcomes and a logistic outcome model, where the augmented estimator demonstrates its doubly robust property, while exhibiting higher precision than all non-augmented weighting estimators and near-identical precision to G-computation. We describe the extension of our estimator to the setting with unavailable individual participant data for the external control, illustrating it through an applied example. Our findings reinforce the understanding that entropy balancing-based approaches have desirable properties compared to standard ``modeling'' approaches to weighting, but should be augmented to improve protection against bias and guarantee double robustness.

翻译：外部对照单臂试验对于评估随机对照试验不可行治疗领域的疗效至关重要。在卫生技术评估中，对于不连接的治疗网络，常需采用密切相关的非锚定间接治疗比较研究设计。我们为这两种研究设计提出了统一的因果推断框架。我们开发了一种新颖的估计器，通过拟合条件结果期望模型，增强了基于熵平衡的流行加权方法——匹配调整间接比较。该结果模型的预测与熵平衡MAIC权重相结合。虽然标准MAIC估计器在结果模型非线性时仅具有单稳健性，我们的增强MAIC方法具有双稳健性，能提供更强的模型误设鲁棒性。这在二元结果和逻辑结果模型的模拟研究中得到验证：增强估计器展现了其双稳健特性，同时表现出比所有非增强加权估计器更高的精度，且精度与G-计算近乎相同。我们描述了将估计器扩展到外部对照个体参与者数据不可用场景的扩展方法，并通过应用实例进行说明。我们的研究结果强化了以下认识：与标准“建模”加权方法相比，基于熵平衡的方法具有更优特性，但应通过增强来提高偏倚防护能力并保证双稳健性。