Evaluating causal indirect effects when mediators are left-censored by assay limit of quantification
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中介变量受检测定量下限左删失时的因果间接效应评估
- 研究问题
- 生物中介变量因检测定量下限而出现确定性左删失及非随机缺失时，如何识别、估计并推断自然直接效应和间接效应？
- 方法与思路
- 作者将分数插补与半参数 EM 结合以估计分解似然，讨论代入式及渐近有效估计，并提出数据自适应 m-out-of-n bootstrap 推断。
- 材料陈述的贡献
- 摘要称数值实验显示偏差降低且推断可靠；临床应用提示单克隆抗体降低住院死亡风险，而病毒 RNA 变化仅中介较小比例的总效应。
- 阅读关注点
- 应核对左删失下的额外识别限制、中介分析假设、插补与 EM 的模型要求，以及自适应 bootstrap 的选择规则和有效性证明。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出识别条件或临床效应数值，应用中的中介结论仍依赖未展示的假设。
原文摘要
Causal mediation analysis is essential for disentangling the mechanisms by which investigational therapeutic and preventive agents impact clinical outcomes. However, the measurement of biological mediators is often subject to left-censoring by technical measurement limitations, most commonly an assay's limit of quantification. This form of censoring can pose severe challenges for both identification and estimation of causal mediation estimands, particularly when the censoring mechanism is deterministic and the resulting missingness is missing not at random (MNAR) or nonignorable. Motivated by the question of assessing the role of viral RNA in the action mechanism of monoclonal antibody therapies for COVID-19 in the Accelerating COVID-19 Therapeutics and Vaccine (ACTIV)-2 platform trial, we develop a semi-parametric framework for estimation of the natural direct and indirect effects when the mediator of interest is partially subject to this form of left-censoring. Our proposed strategy combines fractional imputation with a semi-parametric EM algorithm to flexibly estimate key components of the factorized data likelihood. Applying the proposed strategy to circumvent the left-censoring, we discuss both traditional plug-in and asymptotically efficient estimators of the direct and indirect effect estimands, introducing a data-adaptive $m$-out-of-$n$ bootstrap for robust inference under the imputation procedure. We demonstrate in numerical experiments that our approach significantly reduces bias and allows for reliable inference. An application to data from the ACTIV-2 platform trial confirms that monoclonal antibody therapies reduce the risk of hospitalization and death due to COVID-19, while suggesting that changes in viral RNA mediate only a modest proportion of the overall treatment effect.