Dealing with positivity violations in mediation analysis via weighted controlled effects, with application to assessing immune correlates of protection in antigen-experienced participants
用加权控制效应处理中介分析的正值性违背：既往抗原暴露人群的免疫保护关联应用AI 解读 ↓
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用加权控制效应处理中介分析的正值性违背：既往抗原暴露人群的免疫保护关联应用
- 研究问题
- 论文研究已有抗原暴露人群中，免疫标志物干预受基线水平限制时，如何调整控制风险目标以应对中介分析的正值性违背。
- 方法与思路
- 作者提出加权控制风险，针对具有预设标志物水平可达概率的子人群，并推广到相关子人群的风险对比，再开展模拟和试验重分析。
- 材料陈述的贡献
- 摘要称，该方法使控制风险分析适应既往暴露人群，通过模拟考察估计量有效性，并用于 COVAIL 试验中和抗体与保护关系的重分析。
- 阅读关注点
- 应核对子人群与可达概率的定义、加权后仍需满足的识别条件，以及不同标志物水平的风险对比是否对应同一个目标人群。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未列出完整识别条件，改变目标人群不等于恢复全体人群原控制效应的识别。
原文摘要
Causal mediation analysis has become an important and increasingly used framework for evaluating candidate immune response biomarkers in vaccine research. A controlled effects approach has been proposed to estimate controlled risk curves under a counterfactual scenario in which the entire study population is vaccinated and their post-vaccination immune responses are set to a range of fixed levels. This framework performs well when the study population is antigenically naïve, that is, individuals have not been previously exposed to the antigen, as is common in HIV-1 vaccine research and during the early phases of the COVID-19 pandemic. However, the controlled effects framework becomes more challenging to apply in antigen-experienced populations, where prior vaccination or infection has occurred, as in the case of influenza, dengue, and more recent phases of the COVID-19 pandemic. In such settings, a key identification assumption for valid causal mediation analysis, the positivity assumption, is violated: it is no longer plausible to conceive of a hypothetical intervention that sets a post-vaccination immune marker to a fixed level below an individual's baseline immune level. In this article, we introduce a weighted controlled risk approach that targets a subpopulation for whom there is a prespecified probability of attaining a post-vaccination immune marker level. We further generalize this framework to study contrasts of controlled risks for relevant subpopulations. We demonstrate the validity of the proposed estimators through simulation studies and apply the method to reanalyze post-vaccination neutralizing antibody titers against Omicron BA.4/BA.5 as an immune correlate of COVID-19 in the Coronavirus Variant Immunologic Landscape (COVAIL) trial. R code to implement the proposed method can be found on Github: https://github.com/Qijia-He/weighted_CVE.