Richardson Brian D, Blette Bryan S, Gilbert Peter B, Hudgens Michael G
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
Biometrics. 2025 Apr 2;81(2). doi: 10.1093/biomtc/ujaf045.
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.
在推断暴露因素对感兴趣结局的边际效应时，混杂因素和暴露测量误差可能会引入偏差。虽然有针对每种偏差来源的广泛方法，但混杂因素和暴露测量误差经常同时出现，因此需要能同时处理它们的方法。本文在经典加性测量误差下推导了校正得分方法，以便仅使用测量变量来推断边际暴露效应。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。这些估计量被证明是一致且渐近正态的，并且双重稳健估计量表现出其同名特性。如模拟研究所示，在R包mismex中实现的这些方法在有限样本中，无论是存在混杂因素还是测量误差的情况下，都表现良好。所提出的双重稳健估计量被应用于利用HVTN 505预防性疫苗试验的数据，研究两种生物标志物对HIV-1感染的影响。
