Estimation of controlled direct effects on a dichotomous outcome using logistic structural direct effect models
利用逻辑斯蒂结构直接效应模型估计二元结局的受控直接效应AI 解读 ↓
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利用逻辑斯蒂结构直接效应模型估计二元结局的受控直接效应
- 研究问题
- 如何评估暴露对二元结局不经由指定中介产生的影响，并处理暴露可能影响中介与结局之间混杂因素的情形？
- 方法与思路
- 作者发展与逻辑斯蒂结构直接效应模型相关的新因果模型，在两类关系的混杂因素均已测量的假设下研究识别与有效估计。
- 材料陈述的贡献
- 摘要称新模型缓解相关模型的估计困难，模拟表现适当，并讨论病例对照研究扩展及对时变混杂调整的启示。
- 阅读关注点
- 应核对受控直接效应的定义、完整识别条件、估计程序，以及连续中介、暴露后混杂和病例对照抽样的处理方式。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；已测量全部相关混杂因素是摘要明确的假设，其他条件及模拟数值未说明。
原文摘要
We consider the problem of assessing whether an exposure affects a dichotomous outcome other than by modifying a given mediator. The standard approach, logistic regression adjusting for both exposure and the mediator, is known to be biased in the presence of confounders for the mediator-outcome relationship. Because additional regression adjustment for such confounders is only justified when they are not affected by the exposure, inverse probability weighting has been advocated, but is not ideally tailored to mediators that are continuous or have strong measured predictors. We overcome this limitation by developing inference for a novel class of causal models that are closely related to Robins’ logistic structural direct effect models, but do not inherit their difficulties of estimation. We study identification and efficient estimation under the assumption that all confounders for the exposure-outcome and mediator-outcome relationships have been measured, and find adequate performance in simulation studies. We discuss extensions to case-control studies and relevant implications for the generic problem of adjustment for time-varying confounding.