Mediation Analysis in the Presence of Sample Selection Bias with an Application to Disparities in Liver Transplantation Listing
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样本选择偏差下的中介分析：肝移植候选名单差异的应用
- 研究问题
- 当只有完成评估的患者进入决策样本时，如何识别面向全部转诊患者的直接、间接与路径特定效应，纠正选择偏差？
- 方法与思路
- 作者利用因果有向无环图推导重加权中介公式的图形识别条件，并结合转诊人群信息分析社会经济地位的影响。
- 材料陈述的贡献
- 摘要称给出存在样本选择时多类中介效应的识别条件，并在肝移植案例中允许选择依赖种族、性别、年龄等特征。
- 阅读关注点
- 应核对目标人群、选择机制与中介路径的定义，重加权所需数据及条件，以及临床案例是否满足提出的图形要求。
- 阅读边界
- 这是 arXiv 预印本，不能确认同行评审或期刊接收；摘要未展示具体因果图、识别条件或效应数值，校正有效性仍需核对。
原文摘要
The study of disparities in the liver transplantation process may focus on quantifying causal effects, particularly the average, direct, or indirect effects of various social determinants of health on being listed as a candidate for transplant. Selection bias arises when the data sample does not represent the target population, defined here as all individuals referred to the transplant clinic. Listing decisions are made for the subset of patients who complete the evaluation process, who may differ systematically from the referred population. There is evidence that selection is associated with patient characteristics that also impact outcomes. Using data only from the selected population may yield biased causal effect estimates. However, incorporating data from the referred population allows for analytic correction. This correction leverages hypothesized causal relationships among selection, the outcome (getting listed), exposures, and mediators. Using directed acyclic graphs (DAGs), we establish graphical conditions under which a reweighted mediation formula identifies effect of interest - direct, indirect, and path-specific effects - in the presence of sample selection. In a clinical case study, we investigate mediated and direct effects of a patient's socioeconomic position on being listed for transplant, allowing selection to depend on race, gender, age, and other social determinants.