操作剂量：容量限制对实验设计和解释的影响
Operational Dosage: Implications of Capacity Constraints for the Design and Interpretation of Experiments
AI总结：
本文研究了服务干预随机对照试验中容量限制对实验设计和结果的影响，指出操作剂量的存在导致治疗效果随样本量增加而下降，统计功效在中等样本量时达到峰值。
摘要：
我们研究了评估服务干预影响的随机对照试验（RCTs），例如教师或顾问主动联系有风险的学生，医疗提供者通过电话或短信提供用药依从性支持，或社会工作者进行家庭访问。服务干预的一个显著特征是它们由一个容量受限的资源（教师、医疗提供者或社会工作者）提供，其有限的可用性会引发因果推断问题。由于参与者共享有限的服务容量，增加更多参与者可能会减少其他人所接受的服务的及时性或强度，从而在参与者之间产生干扰。这会引入治疗本身的隐藏变异，我们称之为操作剂量。我们使用排队论技术构建了服务干预的数学模型，并研究了容量限制对实验结果的影响。我们的主要发现是，治疗效果既取决于容量也取决于样本量，并且在达到临界阈值后，随着样本量的增加而减少。有趣的是，一个推论是服务干预RCTs的统计功效在中等样本量时达到峰值——这直接反驳了传统统计功效计算中假设样本量增加时统计功效单调增加的假设。我们通过模拟来实例化我们的见解，这些模拟是针对肯尼亚一项评估结核病患者行为健康干预的真实世界试验进行校准的。我们的模拟结果表明，具有高服务容量但有限样本量的试验可以获得与服务容量较低但样本量大的试验相同的统计功效。总的来说，我们的结果突显了在实验设计中选择容量的重要性，并提供了一个解释实验为何可能无法复制或在扩大规模时表现不佳的机制。
英文摘要：
We study RCTs that evaluate the impact of service interventions, for example, teachers or advisors conducting proactive outreach to at-risk students, medical providers giving medication adherence support by calling or texting, or social workers that conduct home visits. A defining feature of service interventions is that they are delivered by a capacity-constrained resource -- teachers, healthcare providers, or social workers -- whose limited availability creates causal inference complications. Because participants share a finite service capacity, adding more participants can reduce the timeliness or intensity of the service that others receive, introducing interference across participants. This generates hidden variation in the treatment itself, which we term operational dosage. We provide a mathematical model of service interventions using techniques from queueing theory and study the impact of capacity constraints on experimental outcomes. Our main insight is that treatment effects are both capacity- and sample-size-dependent, as well as decreasing in sample size once a critical threshold is exceeded. Interestingly, an implication is that statistical power of service intervention RCTs peaks at intermediate sample sizes -- directly contradicting conventional power calculations that assume monotonically increasing power with sample size. We instantiate our insights using simulations calibrated to a real-world trial evaluating a behavioral health intervention for tuberculosis patients in Kenya. Our simulation results suggest that a trial with high service capacity but limited sample size can obtain the same statistical power as a trial with lower service capacity but large sample size. Taken together, our results highlight the importance of capacity selection in experiment design and provide a mechanism for why experiments may fail to replicate or perform at scale.
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