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. We present simulation results and an empirical application 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定的一组个人的现有服务的干预措施,因此,这种治疗会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开发了因果估计值和反概率估计值(IPW),采用通用估计方程法,调整IPW重量估计错误的标准错误。虽然我们的重点是更一般的分组RCT,但方法也适用于非分组RCT。我们利用大规模RCT测试幼儿服务对儿童认知发展分数的影响的数据,提出模拟结果和实验应用。