Causal mediation analysis is widely used in health science research to evaluate the extent to which an intermediate variable explains an observed exposure-outcome relationship. However, the validity of analysis can be compromised when the exposure is measured with error, which is common in health science studies. This article investigates the impact of exposure measurement error on assessing mediation with a failure time outcome, where a Cox proportional hazard model is considered for the outcome. When the outcome is rare with no exposure-mediator interaction, we show that the unadjusted estimators of the natural indirect and direct effects can be biased into either direction, but the unadjusted estimator of the mediation proportion is approximately unbiased as long as measurement error is not large or the mediator-exposure association is not strong. We propose ordinary regression calibration and risk set regression calibration approaches to correct the exposure measurement error-induced bias in estimating mediation effects and to allow for an exposure-mediator interaction in the Cox outcome model. The proposed approaches require a validation study to characterize the measurement error process between the true exposure and its error-prone counterpart. We apply the proposed approaches to the Health Professionals Follow-up study to evaluate extent to which body mass index mediates the effect of vigorous physical activity on the risk of cardiovascular diseases, and assess the finite-sample properties of the proposed estimators via simulations.

翻译：因果中介分析广泛应用于健康科学研究，以评估中间变量解释暴露-结局关联的程度。然而，当暴露变量存在测量误差时（这在健康科学研究中较为常见），分析的有效性可能受到损害。本文探讨了暴露测量误差对以失败时间结果作为中介效应评估的影响，其中结局采用Cox比例风险模型建模。当结局为稀有事件且不存在暴露-中介交互作用时，我们证明未经校正的自然间接效应和直接效应估计量可能产生双向偏倚，但只要测量误差不大或中介-暴露关联强度较弱，未经校正的中介比例估计量近似无偏。我们提出普通回归校准和风险集回归校准方法，用于校正暴露测量误差导致的中介效应估计偏倚，并允许Cox结局模型中存在暴露-中介交互作用。所提出的方法需要验证性研究来刻画真实暴露与其易错测量值之间的测量误差过程。我们将所提出的方法应用于健康专业人员随访研究，以评估体质指数在剧烈体力活动与心血管疾病风险关联中的中介效应，并通过模拟研究评估所提估计量的有限样本性质。