Structural Mean Effects of Noncompliance
不依从情形下的结构均值效应AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究随机试验不依从下的剂量因果效应、潜在安慰剂反应与治疗效应异质性。
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不依从情形下的结构均值效应
- 研究问题
- 随机安慰剂对照试验存在不依从时，如何估计剂量效应，并允许治疗组实际暴露与潜在安慰剂反应之间存在结构性交互？
- 方法与思路
- 作者在结构模型中引入依从性选择模型以容纳跨试验组变量的交互，并识别不同误差类型的方差分量以描述效应异质性。
- 材料陈述的贡献
- 摘要称结构均值模型蕴含可识别且可检验的选择模型；血压应用中报告显著交互，较差安慰剂反应亚组的高暴露效果更强。
- 阅读关注点
- 应核对潜在安慰剂反应与依从性选择模型的识别限制、次级模型如何检验，以及效应异质性度量和模拟验证的具体设计。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；随机分组并不足以说明全部识别条件，交互效应的大小、稳健性及外推范围未提供。
原文摘要
A randomized, placebo-controlled blood pressure (BP) reduction trial has recorded compliance measures by electronic monitoring. Causal questions regarding dose effects are of interest, but one often wants to avoid the assumption that compliance quantiles on both randomized arms are comparable. Structural mean models (SMMs) do avoid this assumption; however, they do not allow for interaction effects between variables observed on different randomized arms. Such an interaction between observed exposure on the treatment arm and latent treatmentfree response (i. e., placebo response) was suggested by the data. Building on structural models, we propose an approach that makes this possible. To allow for a structural interaction effect with potential placebo response, we invoke a compliance selection model. It turns out that SMMs imply identifiable selection models; hence the secondary model is testable. Next, to assess the amount of variation explained by the structural model, we identify separate variance components for different error types. This yields a measure of the variation in treatment effects over individuals. In addition, it allows us to compare the fit of different plausible structural models. On the BP data, we find a significant interaction effect: Higher exposure levels are estimated to have more effect in subgroups with poorer placebo response. Finite-sample properties of the proposed estimators are verified through simulation.