Analyzing Two-Stage Experiments in the Presence of Interference
Analyzing Two-Stage Experiments in the Presence of Interference
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DOI：
10.1080/01621459.2017.1323641
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发表时间：
2018-01-01
影响因子：
3.7
通讯作者：
Feller, Avi
中科院分区：
文献类型：
--
作者：
Basse, Guillaume;Feller, Avi
两阶段随机化是一种在存在干扰的情况下评估治疗效果的有效设计；即，当一个人的治疗分配影响另一个人的结果时。我们鼓舞人心的例子是一项两阶段随机试验，评估了费城学区减少学生旷课的干预措施。在这项实验中，有多名学生的家庭首先被分配到治疗组或对照组；然后，在接受治疗的家庭中，一个学生被随机分配到治疗组。使用这个例子，我们强调了在实践中分析两阶段实验的关键考虑因素。我们的第一个贡献是解决当家庭规模不同时出现的额外复杂性；在这种情况下，研究人员必须决定是赋予家庭同等的权重还是给予个人同等的权重。我们为一大类个人和家庭加权估计提出了无偏估计，并给出了相应的理论方差和估计方差。我们的第二个贡献是将分析两阶段设计的两种常见方法联系起来：线性回归和随机化推理。我们表明，在适当选择标准误差的情况下，这两种方法产生相同的点和方差估计，对于复杂的随机化方案，这有点令人惊讶。最后，我们探索了合并协变量以提高精度的选项。我们通过模拟研究证实了我们的分析结果，并将这些方法应用到出勤率研究中，发现了实质性的溢出效应。
Two-stage randomization is a powerful design for estimating treatment effects in the presence of interference; that is, when one individual's treatment assignment affects another individual's outcomes. Our motivating example is a two-stage randomized trial evaluating an intervention to reduce student absenteeism in the School District of Philadelphia. In that experiment, households with multiple students were first assigned to treatment or control; then, in treated households, one student was randomly assigned to treatment. Using this example, we highlight key considerations for analyzing two-stage experiments in practice. Our first contribution is to address additional complexities that arise when household sizes vary; in this case, researchers must decide between assigning equal weight to households or equal weight to individuals. We propose unbiased estimators for a broad class of individual- and household-weighted estimands, with corresponding theoretical and estimated variances. Our second contribution is to connect two common approaches for analyzing two-stage designs: linear regression and randomization inference. We show that, with suitably chosen standard errors, these two approaches yield identical point and variance estimates, which is somewhat surprising given the complex randomization scheme. Finally, we explore options for incorporating covariates to improve precision. We confirm our analytic results via simulation studies and apply these methods to the attendance study, finding substantively meaningful spillover effects.