Doubly robust augmented weighting estimators for the analysis of externally controlled single-arm trials and unanchored indirect treatment comparisons
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外部对照单臂试验与非锚定间接治疗比较的双重稳健增广加权估计
- 研究问题
- 外部对照单臂试验和非锚定间接治疗比较如何在统一因果框架下估计疗效，并提高熵平衡加权对模型错误的稳健性？
- 方法与思路
- 作者将条件结局均值模型的预测与熵平衡 MAIC 权重结合，构造增广估计，并讨论外部对照个体数据不可用时的扩展。
- 材料陈述的贡献
- 摘要称增广方法具有双重稳健性；在二元结局模拟中，其精度高于非增广加权方法且接近 G-computation，并提供应用示例。
- 阅读关注点
- 应核对跨来源因果识别、双重稳健性的两类模型条件，以及仅有外部汇总数据时可用信息如何支持权重和增广计算。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未核实。摘要未给出完整条件或应用数值，特定模拟中的精度比较不能推为普遍优势。
原文摘要
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.