Confounding and exposure measurement error can introduce bias when drawing inference about the marginal effect of an exposure on an outcome of interest. While there are broad methodologies for addressing each source of bias individually, confounding and exposure measurement error frequently co-occur and there is a need for methods that address them simultaneously. In this paper, corrected score methods are derived under classical additive measurement error to draw inference about marginal exposure effects using only measured variables. Three estimators are proposed based on g-formula, inverse probability weighting, and doubly-robust estimation techniques. The estimators are shown to be consistent and asymptotically normal, and the doubly-robust estimator is shown to exhibit its namesake property. The methods, which are implemented in the R package mismex, perform well in finite samples under both confounding and measurement error as demonstrated by simulation studies. The proposed doubly-robust estimator is applied to study the effects of two biomarkers on HIV-1 infection using data from the HVTN 505 preventative vaccine trial.

翻译：混杂和暴露测量误差在推断暴露对目标结果的边际效应时可能引入偏倚。尽管已有广泛的方法论可单独处理每种偏倚来源，但混杂与暴露测量误差常同时出现，因此需要能同时处理二者的方法。本文基于经典加性测量误差推导了校正得分方法，仅使用测量变量即可进行边际暴露效应的推断。基于g公式、逆概率加权和双重稳健估计技术提出了三种估计量。这些估计量被证明具有一致性和渐近正态性，且双重稳健估计量展现出其同名特性。通过模拟研究表明，在混杂和测量误差同时存在的有限样本中，这些方法（已实现于R包mismex）表现良好。所提出的双重稳健估计量被应用于利用HVTN 505预防性疫苗试验数据研究两种生物标志物对HIV-1感染的影响。