跨中心数据联合估计治疗效果，提升临床试验分析精度。
Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis
- 用联邦学习框架整合多中心数据，优化因果效应估计
- 在不同样本量和协变量分布下，多轮通信方法方差更小
- 适合多中心随机对照试验的疗效评估，尤其数据异质性强时
我们研究联邦因果推断，旨在从多个中心的分散数据中估计治疗效应。对比了三类基于插件G-公式法的平均治疗效应（ATE）估计器：简单元分析、单轮与多轮联邦学习。后者利用完整数据学习结果模型，虽需更多通信开销，但性能更优。针对随机对照试验（RCT），我们推导了线性模型下这些估计器的渐近方差。结果为不同场景（如样本量异质、协变量分布差异、治疗分配方式不同、中心效应）下的估计器选择提供了实用指导。仿真研究验证了理论结论。
原文摘要 · Abstract (English)
We study Federated Causal Inference, an approach to estimate treatment effects from decentralized data across centers. We compare three classes of Average Treatment Effect (ATE) estimators derived from the Plug-in G-Formula, ranging from simple meta-analysis to one-shot and multi-shot federated learning, the latter leveraging the full data to learn the outcome model (albeit requiring more communication). Focusing on Randomized Controlled Trials (RCTs), we derive the asymptotic variance of these estimators for linear models. Our results provide practical guidance on selecting the appropriate estimator for various scenarios, including heterogeneity in sample sizes, covariate distributions, treatment assignment schemes, and center effects. We validate these findings with a simulation study.
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