Optimality of Matched-Pair Designs in Randomized Controlled Trials
证明在随机对照试验中，当所有单元被分配处理的概率均为二分之一时，某种配对设计能最大程度提高平均处理效应估计的统计精度；基于十个试验数据的模拟显示，该设计平均降低标准误10%。
In randomized controlled trials, treatment is often assigned by stratified randomization. I show that among all stratified randomization schemes that treat all units with probability one half, a certain matched-pair design achieves the maximum statistical precision for estimating the average treatment effect. In an important special case, the optimal design pairs units according to the baseline outcome. In a simulation study based on datasets from ten randomized controlled trials, this design lowers the standard error for the estimator of the average treatment effect by 10 percent on average, and by up to 34 percent, relative to the original designs.