The test-negative design (TND) is a resource-efficient observational study design that can assess vaccine effectiveness and exposure-proximal immune correlates of disease. The TND enrolls symptomatic individuals seeking diagnostic testing and compares case status by an exposure variable, such as vaccination status or immune marker level, that is measured at testing. While the TND reduces confounding by healthcare-seeking behavior, other sources of confounding may remain. TND studies may also have missing data in the exposure variable due to incomplete records or two-phase sampling designs. We present a targeted maximum likelihood estimation approach involving a semiparametric logistic regression model that targets a causal conditional risk ratio of symptomatic disease in the healthcare-seeking population. Under causal and missing at random assumptions, our method produces an efficient, asymptotically linear estimator that provides flexible, data-driven confounding control and valid causal inference when analyzing TND studies with missing exposure variable data. We evaluate our method's finite sample properties using plasmode simulations of a two-phase TND immune correlates study. We also apply our method to assess COVID-19 vaccine effectiveness and antibody marker correlates of COVID-19 from TND study cohorts derived from the Moderna Coronavirus Efficacy phase 3 trial.

翻译：检测阴性设计是一种资源高效的观察性研究设计，可用于评估疫苗效果及疾病暴露近端免疫相关性指标。该设计招募有症状并寻求诊断检测的个体，通过检测时测量的暴露变量（如疫苗接种状态或免疫标志物水平）比较病例状态。虽然检测阴性设计减少了医疗寻求行为导致的混杂因素，但仍可能存在其他混杂来源。此外，由于记录不完整或两阶段抽样设计，检测阴性设计研究可能面临暴露变量数据缺失的问题。我们提出一种基于半参数逻辑回归模型的靶向最大似然估计方法，该模型以医疗寻求人群中症状性疾病的因果条件风险比为靶目标。在因果性及随机缺失假设下，该方法可生成高效、渐近线性的估计量，在对存在暴露变量数据缺失的检测阴性设计研究进行分析时，能实现灵活的数据驱动式混杂控制及有效的因果推断。我们通过基于两阶段检测阴性设计免疫相关性研究构建的plasmode模拟，评估了该方法在有限样本下的性能。同时，我们应用该方法分析由Moderna新冠病毒效力3期临床试验衍生队列构成的检测阴性设计研究数据，评估COVID-19疫苗效果及其抗体标志物相关性。