Case Definition and Design Sensitivity
病例定义与设计敏感度AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究非随机暴露的因果判断及未测混杂敏感性分析。
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病例定义与设计敏感度
- 研究问题
- 在病例对照研究中，病例定义的宽窄如何影响结论对未测混杂的敏感性，能否从数据中选择更有利的病例定义？
- 方法与思路
- 作者结合设计敏感度这一渐近工具、有限样本模拟和实例分析，并讨论控制多重检验的自适应病例定义选择方法。
- 材料陈述的贡献
- 摘要称在有利条件下，较窄病例定义能提高设计敏感度及敏感性分析的功效，并给出相应R实现的信息。
- 阅读关注点
- 应核对较窄定义提高设计敏感度的条件、匹配后未测偏差的建模方式，以及自适应选择如何控制多重检验。
- 阅读边界
- 材料为版本未注明的 OpenAlex 索引摘要；未给出有利条件的具体内容，病例定义收窄不必然提高功效或消除混杂。
原文摘要
In a case-referent study, cases of disease are compared to non-cases with respect to their antecedent exposure to a treatment in an effort to determine whether exposure causes some cases of the disease. Because exposure is not randomly assigned in the population, as it would be if the population were a vast randomized trial, exposed and unexposed subjects may differ prior to exposure with respect to covariates that may or may not have been measured. After controlling for measured pre-exposure differences, for instance by matching, a sensitivity analysis asks about the magnitude of bias from unmeasured covariates that would need to be present to alter the conclusions of a study that presumed matching for observed covariates removes all bias. The definition of a case of disease affects sensitivity to unmeasured bias. We explore this issue using: (i) an asymptotic tool, the design sensitivity, (ii) a simulation for finite samples, and (iii) an example. Under favorable circumstances, a narrower case definition can yield an increase in the design sensitivity, and hence an increase in the power of a sensitivity analysis. Also, we discuss an adaptive method that seeks to discover the best case definition from the data at hand while controlling for multiple testing. An implementation in R is available as SensitivityCaseControl.