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Transporting experimental results with entropy balancing
Statistics in Medicine ( IF 2 ) Pub Date : 2021-05-20 , DOI: 10.1002/sim.9031
Kevin P Josey 1 , Seth A Berkowitz 2 , Debashis Ghosh 1 , Sridharan Raghavan 3, 4
Affiliation  

We show how entropy balancing can be used for transporting experimental treatment effects from a trial population onto a target population. This method is doubly robust in the sense that if either the outcome model or the probability of trial participation is correctly specified, then the estimate of the target population average treatment effect is consistent. Furthermore, we only require the sample moments of the effect modifiers drawn from the target population to consistently estimate the target population average treatment effect. We compared the finite-sample performance of entropy balancing with several alternative methods for transporting treatment effects between populations. Entropy balancing techniques are efficient and robust to violations of model misspecification. We also examine the results of our proposed method in an applied analysis of the Action to Control Cardiovascular Risk in Diabetes Blood Pressure trial transported to a sample of US adults with diabetes taken from the National Health and Nutrition Examination Survey cohort.

中文翻译:

使用熵平衡传输实验结果

我们展示了如何使用熵平衡将实验治疗效果从试验人群转移到目标人群。这种方法具有双重稳健性,即如果正确指定了结果模型或试验参与概率,则目标人群平均治疗效果的估计值是一致的。此外,我们只需要从目标人群中提取的效应修饰符的样本矩来一致地估计目标人群的平均治疗效果。我们将熵平衡的有限样本性能与几种在人群之间传输治疗效果的替代方法进行了比较。熵平衡技术对于违反模型错误规范是有效且稳健的。
更新日期:2021-07-19
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