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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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AI 根据所提供的论文材料归类:摘要研究随机试验中治疗所致异常反应的随机化检验及效应置信区间,直接对应因果效应推断。

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关注这篇论文的作者
Paul R Rosenbaum
Jeffrey H Silber

治疗的异常效应

研究问题
随机试验中,如何判断某种治疗是否导致严重异常反应,同时利用异常发生次数与严重程度,并处理退出治疗的解释困难?
方法与思路
作者提出结合异常次数和幅度的随机化检验,讨论事后选择异常维度的后果,并通过反演检验构造加性异常效应区间。
材料陈述的贡献
摘要称检验在极端情形下退化为 Fisher 精确检验或 Wilcoxon 秩和检验,且仅响应异常效应,不受其他治疗效应影响。
阅读关注点
应核对异常反应的定义、精确检验对应的效应假设,以及事后挑选维度和反演置信区间所需的处理与限制。
阅读边界
仅据 OpenAlex 索引摘要,稿件版本未注明,不能推断刊本变化;“仅响应异常效应”的条件和临床例证的检验结果未说明。

原文摘要

Often, the protocol for a randomized controlled trial states that if a patient's health deteriorates substantially in certain specific ways, then the study treatments will no longer be considered adequate medical care, and the patient will be taken off the study treatments and given the needed care. Aberrant responses of this sort present special problems of interpretation. The few such patients may have received only limited exposure to the study treatments, but also may have experienced some of the worst outcomes observed in the trial. The most basic question is whether one of the treatments tends to cause such aberrant responses, quite apart from any other beneficial or harmful effects that treatment may have. Common practice is to reduce aberrant responses to binary events and apply Fisher's exact test for a 2 × 2 table, but this approach discards the available information about the magnitude of the aberration. We propose a new randomization test for this problem that uses information about both the number of aberrations and their magnitude. In extreme situations, the new test reduces to either Fisher's exact test or Wilcoxon's rank sum test, but in common situations, the new test has features of both methods. Importantly, this exact test reacts only to aberrant effects; it is completely unaffected by treatment effects that do not produce aberrations. When aberrant responses have several aspects, we examine the consequences of selecting, after the fact, the one aspect exhibiting the greatest aberration, and applying the test to that aspect. For a single response variable, a confidence interval is proposed for an additive aberrant effect; the interval is constructed by inverting the test, and a multivariate extension is briefly mentioned. We illustrate the use of the test in the ACE-Inhibitor After Anthracycline (AAA) randomized trial, which was aimed at preserving cardiac function in children treated for cancer. Seven of 135 children were removed from the trial due to substantial cardiac declines; 6 of these were on placebo, 1 of whom subsequently died.

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