论文标题
连续实验的匹配过程，它迭代地学习了协变量提高功率
A Matching Procedure for Sequential Experiments that Iteratively Learns which Covariates Improve Power
论文作者
论文摘要
我们提出了一种动态分配程序，该程序在测量平均治疗效果的过程中，在利用某些受试者以前的评估反应的顺序随机试验中，可以提高功率和效率。受试者依次到达，并且被随机或与先前随机的受试者配对，并进行了替代治疗。配对是通过动态匹配标准进行的，迭代地学习哪些特定协变量对响应很重要。我们开发了平均治疗效果以及精确测试的估计器。我们说明了在模拟方案和临床试验数据集中，我们的方法对其他分配程序的效率和功率提高。可以使用R型“ SEQ Expmatch”供从业人员使用。
We propose a dynamic allocation procedure that increases power and efficiency when measuring an average treatment effect in sequential randomized trials exploiting some subjects' previous assessed responses. Subjects arrive sequentially and are either randomized or paired to a previously randomized subject and administered the alternate treatment. The pairing is made via a dynamic matching criterion that iteratively learns which specific covariates are important to the response. We develop estimators for the average treatment effect as well as an exact test. We illustrate our method's increase in efficiency and power over other allocation procedures in both simulated scenarios and a clinical trial dataset. An R package "SeqExpMatch" for use by practitioners is available.
