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. This article investigates the impact of exposure measurement error on assessing mediation with a survival outcome, based on the Cox proportional hazards outcome model. When the outcome is rare and there is no exposure-mediator interaction, we show that the uncorrected estimators of the natural indirect and direct effects can be biased into either direction, but the uncorrected estimator of the mediation proportion is approximately unbiased as long as the measurement error is not large or the mediator-exposure association is not strong. We develop ordinary regression calibration and risk set regression calibration approaches to correct the exposure measurement error-induced bias when estimating mediation effects and allowing for an exposure-mediator interaction in the Cox outcome model. The proposed approaches require a validation study to characterize the measurement error process. We apply the proposed approaches to the Health Professionals Follow-up study to evaluate extent to which reduced body mass index mediates the protective effect of vigorous physical activity on the risk of cardiovascular diseases, and compare the finite-sample properties of the proposed estimators via simulations.

翻译：中介分析在健康科学研究中被广泛用于评估中间变量在多大程度上解释观察到的暴露-结局关系。然而，当暴露测量存在误差时，分析的有效性可能受到损害。本文基于Cox比例风险结局模型，研究了暴露测量误差对评估生存结局中介效应的影响。当结局罕见且不存在暴露-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向任一方向产生偏倚，但中介比例的未校正估计量近似无偏，前提是测量误差不大或中介-暴露关联较弱。我们提出了普通回归校准和风险集回归校准方法，用于在估计中介效应时校正暴露测量误差引起的偏倚，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测量误差过程。我们将所提出的方法应用于卫生专业人员随访研究，以评估降低体重指数在多大程度上中介了剧烈体育活动对心血管疾病风险的保护效应，并通过模拟比较了所提出估计量的有限样本性质。