Exact, Nonparametric Inference When Doses Are Measured With Random Errors
剂量含随机测量误差时的精确非参数推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要围绕暴露效应、不同剂量和匹配零剂量对照开展推断，与因果推断设计相关。
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剂量含随机测量误差时的精确非参数推断
- 研究问题
- 将不同暴露剂量的个体与零剂量对照匹配时，如何利用有测量误差的剂量推断真实剂量与响应之间的线性关系？
- 方法与思路
- 作者利用剂量误差关于零对称及零剂量对照构造非参数推断；匹配对使用符号秩检验，多对照匹配则处理组内依赖。
- 材料陈述的贡献
- 摘要称可获得精确、分布无关的区间与检验及一致点估计，并为多对照匹配推导适当的精确分布和大样本近似。
- 阅读关注点
- 应核对真实剂量与响应关系的假设、误差对称性和匹配设计要求，以及多对照下组内依赖如何改变秩统计量分布。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；对称误差不涵盖系统性少报，摘要也未完整说明将暴露关系解释为因果效应的条件。
原文摘要
Studies that estimate the effects of exposure to a possibly harmful agent often compare exposed subjects who received varied doses with matched controls who received zero dose. If the doses are measured with error, then one may wish to use the fallible doses to estimate a linear relationship between the unobserved true dose and the observed response. If one is willing to assume that the dose errors for exposed subjects are symmetrically distributed about 0—that is, the dose errors are pure errors and not, say, systematic underreporting of exposure—then the presence of zero-dose controls is all that is needed to obtain exact, distribution-free confidence intervals and tests, and consistent point estimates. The method is simpler for matched pairs than for matched sets with two or more matched subjects, and it is illustrated using two studies, one of each kind. With matched pairs, as in this first example, the method uses Wilcoxon's signed rank test as the basis for inference. When there are several zero-dose controls matched to each exposed subject, the familiar null distribution of the signed rank statistic is no longer applicable because of dependence within matched sets, so the appropriate exact distribution and large-sample approximation are developed.