RCTs sometimes test interventions that aim to improve existing services targeted to a subset of individuals identified after randomization. Accordingly, the treatment could affect the composition of service recipients and the offered services. With such bias, intention-to-treat estimates using data on service recipients and nonrecipients may be difficult to interpret. This article develops causal estimands and inverse probability weighting (IPW) estimators for complier populations in these settings, using a generalized estimating equation approach that adjusts the standard errors for estimation error in the IPW weights. While our focus is on more general clustered RCTs, the methods also apply (reduce) to non-clustered RCTs. Simulations show that the estimators achieve nominal confidence interval coverage under the assumed identification conditions. An empirical application demonstrates the methods using data from a large-scale RCT testing the effects of early childhood services on children's cognitive development scores.
翻译:有时,RCT测试旨在改进针对随机分类后确认的一组个人的现有服务的干预措施,因此,这种治疗可能影响服务接受者和所提供服务的组成。由于这种偏差,使用服务接受者和非接受者数据的意图估计可能难以解释。本条款为这些环境中的遵守者群体制定了因果估计值和反概率估计值(IPW),采用通用估计方程方法,调整IPW重量估计错误的标准错误。我们的重点是更普遍的分类RCT,但方法也适用于非分类的RCT。模拟表明,根据假定的识别条件,估计者达到名义信任期覆盖。经验应用展示了使用大规模RCT测试幼儿服务对儿童认知发展分数的影响的数据的方法。