Randomization Inference With Imperfect Compliance in the ACE-Inhibitor After Anthracycline Randomized Trial
蒽环类药物治疗后 ACE 抑制剂试验中不完全依从下的随机化推断AI 解读 ↓
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蒽环类药物治疗后 ACE 抑制剂试验中不完全依从下的随机化推断
- 研究问题
- 在比较依那普利与安慰剂的随机试验中，如何在依从性可能严重偏倚时，利用实际服药信息开展随机化推断？
- 方法与思路
- 作者应用以随机分配为推断依据的工具变量方法，不假定依从性随机，同时承认随机分组与实际用药剂量的区别。
- 材料陈述的贡献
- 摘要称，该分析与意向治疗分析的显著性结论等价，同时反映未实际服药者无法获得药物本身的药理收益。
- 阅读关注点
- 应核对工具变量推断的效应对象、剂量与效应模型、所需假设，以及显著性等价结论对应的检验和备择范围。
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- 仅据版本未注明的 OpenAlex 索引摘要；摘要未给完整识别假设、效应估计或试验结果，不能推断依那普利确有疗效。
原文摘要
Anthracyclines are quite effective at curing certain cancers of childhood, but they may damage the heart. The ACE-Inhibitor After Anthracycline (AAA) study compared enalapril to placebo in a randomized trial in an effort to determine whether treatment with enalapril would preserve or improve cardiac function among children previously treated with anthracylines. As is true in many clinical trials, patient compliance with the study protocol was imperfect; some children took less than the prescribed dose of enalapril or placebo. Most analytical procedures that acknowledge imperfect compliance do so at significant cost, abandoning the tight logic of random assignment. With noncompliance, assignment to enalapril or placebo is randomized, but the dose of enalapril actually received is not, and self-selection effects parallel to those in observational studies can exist and have been documented in some instances. Some researchers advocate adherence to the strict logic of randomization by reporting only, or else strongly emphasizing, the so-called “intent-to-treat” analysis, which makes no use of information about compliance. Other researchers report analyses that are not justified by random assignment and can be subject to substantial biases, such as “per protocol” analyses or “treatment received” analyses. Here we apply a recent proposal for randomization inference with an instrumental variable that uses randomization as the “reasoned basis for inference” in Fisher's phrase. We make no assumption that compliance is random; indeed, compliance may be severely biased. Importantly, the proposed analysis will find a statistically significant effect of the treatment if and only if the intent-to-treat analysis finds a significant effect; yet, unlike intent-to-treat analysis, our analysis acknowledges that a patient assigned to a drug that he or she does not take will not receive the drug's pharmacological benefits.