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.

翻译：因果中介分析对于厘清研究性治疗及预防药物对临床结局的影响机制至关重要。然而，生物中介变量的测量常受技术测量限制（最常见的是检测定量下限）导致左删失。这种删失形式会给因果中介估计量的识别与估计带来严峻挑战，尤其当删失机制为确定性且由此产生的缺失属于非随机缺失（MNAR）或不可忽略缺失时。受评估病毒RNA在抗新冠病毒单克隆抗体治疗作用机制中的角色这一问题的启发（基于加速新冠病毒治疗与疫苗（ACTIV）-2平台试验），我们开发了一个半参数框架，用于在感兴趣的中介变量部分受此类左删失影响时估计自然直接效应与间接效应。所提出的策略将分数插补与半参数EM算法相结合，以灵活估计因子化数据似然的关键组成部分。为规避左删失问题，我们应用该策略讨论了直接效应与间接效应估计量的传统插件估计及其渐近有效估计，并引入了一种数据自适应$m$-out-of-$n$自助法以在插补程序下实现稳健推断。数值实验表明，我们的方法显著降低了偏倚，并允许进行可靠推断。对ACTIV-2平台试验数据的应用证实，单克隆抗体疗法可降低因COVID-19住院和死亡的风险，同时提示病毒RNA的变化仅介导了总体治疗效果中的适度比例。