Zhang Yiwen, Dai Ran, Huang Ying, Prentice Ross, Zheng Cheng
Zilber School of Public Health, University of Wisconsin-Milwaukee.
Department of Biostatistics, University of Nebraska Medical Center.
Ann Appl Stat. 2024 Mar;18(1):125-143. doi: 10.1214/23-aoas1782. Epub 2024 Jan 31.
Systematic measurement error in self-reported data creates important challenges in association studies between dietary intakes and chronic disease risks, especially when multiple dietary components are studied jointly. The joint regression calibration method has been developed for measurement error correction when objectively measured biomarkers are available for all dietary components of interest. Unfortunately, objectively measured biomarkers are only available for very few dietary components, which limits the application of the joint regression calibration method. Recently, for single dietary components, controlled feeding studies have been performed to develop new biomarkers for many more dietary components. However, it is unclear whether the biomarkers separately developed for single dietary components are valid for joint calibration. In this paper, we show that biomarkers developed for single dietary components cannot be used for joint regression calibration. We propose new methods to utilize controlled feeding studies to develop valid biomarkers for joint regression calibration to estimate the association between multiple dietary components simultaneously with the disease of interest. Asymptotic distribution theory for the proposed estimators is derived. Extensive simulations are performed to study the finite sample performance of the proposed estimators. We apply our methods to examine the joint effects of sodium and potassium intakes on cardiovascular disease incidence using the Women's Health Initiative cohort data. We identify positive associations between sodium intake and cardiovascular diseases as well as negative associations between potassium intake and cardiovascular disease.
自我报告数据中的系统测量误差在饮食摄入量与慢性病风险的关联研究中带来了重大挑战，尤其是在联合研究多种饮食成分时。当可获得所有感兴趣饮食成分的客观测量生物标志物时，已开发出联合回归校准方法用于测量误差校正。不幸的是，客观测量的生物标志物仅适用于极少数饮食成分，这限制了联合回归校准方法的应用。最近，对于单一饮食成分，已开展对照喂养研究以开发更多饮食成分的新生物标志物。然而，尚不清楚为单一饮食成分分别开发的生物标志物是否适用于联合校准。在本文中，我们表明为单一饮食成分开发的生物标志物不能用于联合回归校准。我们提出了新方法，利用对照喂养研究来开发用于联合回归校准的有效生物标志物，以同时估计多种饮食成分与感兴趣疾病之间的关联。推导了所提出估计量的渐近分布理论。进行了广泛的模拟以研究所提出估计量的有限样本性能。我们应用我们的方法，使用女性健康倡议队列数据来检验钠和钾摄入量对心血管疾病发病率的联合影响。我们确定了钠摄入量与心血管疾病之间的正相关以及钾摄入量与心血管疾病之间的负相关。
