在移动干预参与的工具变量分析中界定局部平均处理效应
Bounding the local average treatment effect in an instrumental variable analysis of engagement with a mobile intervention
摘要：
在随机试验中估计局部平均处理效应通常需要一个假设，即排除限制，因为我们不愿意排除不可测量的混杂因素。在这种假设下，治疗的任何获益都将通过随机化后变量进行调节，并且不会直接归因于随机化本身或其潜在后代。最近，人们对提供医疗保健支持的移动健康干预产生了兴趣;这种研究可以以单向内容和/或双向内容为特征，后者允许受试者以可以在受试者特定水平上客观测量的方式参与干预（例如，收到响应的文本消息的比例）。因此，很有可能通过干预措施实现的获益部分可以通过接受干预措施内容来解释，部分可以通过参与/响应干预措施内容来解释。因此，当试图描述随机化后参与的平均因果效应时，排除限制几乎肯定会被违反。在本文中，我们提出了一个概念上直观的敏感性分析程序，这种设置，使当地的平均处理效应的急剧变化。大量的模拟研究表明，这种方法具有非常好的有限样本行为，并在灵敏度参数的正确规范下恢复局部平均处理效应。我们将我们的方法应用于一项随机试验，评估2型糖尿病自我护理的短信干预。
英文摘要：
Estimation of local average treatment effects in randomized trials typically requires an assumption known as the exclusion restriction in cases where we are unwilling to rule out unmeasured confounding. Under this assumption, any benefit from treatment would be mediated through the post-randomization variable being conditioned upon, and would be directly attributable to neither the randomization itself nor its latent descendants. Recently, there has been interest in mobile health interventions to provide healthcare support; such studies can feature one-way content and/or two-way content, the latter of which allowing subjects to engage with the intervention in a way that can be objectively measured on a subject-specific level (e.g., proportion of text messages receiving a response). It is hence highly likely that a benefit achieved by the intervention could be explained in part by receipt of the intervention content and in part by engaging with/responding to it. When seeking to characterize average causal effects conditional on post-randomization engagement, the exclusion restriction is therefore all but surely violated. In this paper, we propose a conceptually intuitive sensitivity analysis procedure for this setting that gives rise to sharp bounds on local average treatment effects. A wide array of simulation studies reveal this approach to have very good finite-sample behavior and to recover local average treatment effects under correct specification of the sensitivity parameter. We apply our methodology to a randomized trial evaluating a text message-delivered intervention for Type 2 diabetes self-care.
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