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Abstract：
We present methods for causally interpretable meta-analyses that combine information from multiple randomized trials to draw causal inferences for a target population of substantive interest. We consider identifiability conditions, derive implications of the conditions for the law of the observed data, and obtain identification results for transporting causal inferences from a collection of independent randomized trials to a new target population in which experimental data may not be available. We propose an estimator for the potential outcome mean in the target population under each treatment studied in the trials. The estimator uses covariate, treatment, and outcome data from the collection of trials, but only covariate data from the target population sample. We show that it is doubly robust in the sense that it is consistent and asymptotically normal when at least one of the models it relies on is correctly specified. We study the finite sample properties of the estimator in simulation studies and demonstrate its implementation using data from a multicenter randomized trial.
摘要：
我们提出了因果可解释荟萃分析的方法，该方法结合了来自多个随机试验的信息，以得出具有实质性兴趣的目标人群的因果推论。我们考虑可识别性条件，推导条件对观测数据规律的影响，并获得鉴定结果，用于将一组独立随机试验的因果推断转移到可能没有实验数据的新目标人群。我们提出了在试验中研究的每种治疗方法下，目标人群中潜在结果平均值的估计方法。估计器使用协变量，治疗,以及来自试验收集的结果数据，但只有来自目标群体样本的协变量数据。我们证明，当它所依赖的至少一个模型被正确指定时，它是一致且渐近正态的，从某种意义上说，它是双重鲁棒的。我们在模拟研究中研究了估计器的有限样本属性，并使用多中心随机试验的数据演示了其实现。
