存在网络成员误分类的自我中心网络随机试验中因果溢出效应的估计与推断
Estimation and inference for causal spillover effects in egocentric-network randomized trials in the presence of network membership misclassification
AI总结：
针对自我中心网络随机试验中网络成员误分类导致平均溢出效应估计有偏的问题，结合测量误差与因果推断方法提出偏差校正方案，经模拟和HPTN 037研究验证有效。
摘要：
为利用同伴影响并扩大人群行为改变的范围，行为干预通常依托基于同伴的策略开展。评估这类策略的一种常见研究设计是自我中心网络随机试验（egocentric-network randomized trial, ENRT），在该试验中，接受干预的受试者会被鼓励向其同伴传播信息。平均溢出效应（Average Spillover Effect, ASpE）用于衡量干预对未接受干预、但其结局可能受其他接受干预者影响的受试者的作用。对ASpE的评估依赖于对干扰集（个体可在其中影响彼此结局的集合）的假设与准确测量。正确界定干扰集（如ENRT中的网络）颇具难度，而当测量存在误差时，现有方法估计的干预效应会产生偏差。在社交网络对疾病传播起重要作用的HIV预防研究中，校正由网络误分类导致的ASpE估计偏差，对于准确评估干预的整体影响至关重要。我们结合测量误差与因果推断方法，针对ENRT中记录的是替代网络而非真实网络、且存在关联误分类网络与真实网络的验证数据的情况，对ASpE估计值进行偏差校正。我们通过大规模模拟研究探究了所提方法的有限样本性质，并在HIV预防试验网络（HIV Prevention Trials Network, HPTN）037研究中对方法进行了实例演示。
英文摘要：
To leverage peer influence and increase population behavioral changes, behavioral interventions often rely on peer-based strategies. A common study design that assesses such strategies is the egocentric-network randomized trial (ENRT), in which those receiving the intervention are encouraged to disseminate information to their peers. The Average Spillover Effect (ASpE) measures the impact of the intervention on participants who do not receive it, but whose outcomes may be affected by others who do. The assessment of the ASpE relies on assumptions about, and correct measurement of, interference sets within which individuals may influence one another's outcomes. It can be challenging to properly specify interference sets, such as networks in ENRTs, and when mismeasured, intervention effects estimated by existing methods will be biased. In HIV prevention studies where social networks play an important role in disease transmission, correcting ASpE estimates for bias due to network misclassification is critical for accurately evaluating the full impact of interventions. We combined measurement error and causal inference methods to bias-correct the ASpE estimate for network misclassification in ENRTs, when surrogate networks are recorded in place of true ones, and validation data that relate the misclassified to the true networks are available. We investigated finite sample properties of our methods in an extensive simulation study, and illustrated our methods in the HIV Prevention Trials Network (HPTN) 037 study.
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