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本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。

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分类依据与纠错

AI 根据所提供的论文材料归类:摘要明确讨论潜在依从行为的主层化、意向治疗原则与剂量反应的因果推断。

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代码、数据与补充材料

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关注这篇论文的作者
Hui Jin
Donald B Rubin

扩展部分依从情形下因果推断的主层化方法

研究问题
在服药剂量仅部分依从、且副作用可能破坏盲法的随机试验中,如何区分随机分配效应与实际剂量的因果效应?
方法与思路
作者将对活性药物和安慰剂的不同依从行为纳入主层化框架,遵循意向治疗原则,并重新分析既有试验数据。
材料陈述的贡献
摘要称,该框架明确了从非随机的实际剂量数据推断剂量反应所需的假设,同时指出这些假设在案例中存在争议。
阅读关注点
应核对主层的定义、随机分配与实际服药剂量的关系,以及剂量反应识别假设为何在该试验环境中值得质疑。
阅读边界
仅据版本未注明的 OpenAlex 索引摘要,不能判断具体识别假设是否成立、估计精度或其他试验中的适用性,也不能推断刊本变化。

原文摘要

Many double-blind placebo-controlled randomized experiments with active drugs suffer from complications beyond simple noncompliance. First, the compliance with assigned dose is often partial, with patients taking only part of the assigned dose, whether active or placebo. Second, the blinding may be imperfect in the sense that there may be detectable positive or negative side effects of the active drug, and consequently, simple compliance has to be extended to allow different compliances to active drug and placebo. Efron and Feldman presented an analysis of such a situation and discussed inference for dose–response from the nonrandomized data in the active treatment arm, which stimulated active discussion, including on the role of the intention-to-treat principle in such studies. Here, we formulate the problem within the principal stratification framework of Frangakis and Rubin, which adheres to the intention-to-treat principle, and we present a new analysis of the Efron–Feldman data within this framework. Moreover, we describe precise assumptions under which dose–response can be inferred from such nonrandomized data, which seem debatable in the setting of this example. Although this article only deals in detail with the specific Efron–Feldman data, the same framework can be applied to various circumstances in both natural science and social science.

笔记与分组
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