ABSTRACT

Exposure measurement error (ME) biases exposure-outcome associations. Calibration dietary intake data used in the regression calibration (RC) response to adjust for ME are usually right-skewed, heteroscedastic and with excess zeroes. We proposed three-part RC models to handle these distributional complexities simultaneously, while correcting for ME in fish intake. We applied data from the National Health and Nutrition Examination Survey (NHANES), where long-term intake was measured with food frequency questionnaire (FFQ) in the main study and short-term intake with 24-hour recall (24HR) in the calibration study. In the three-part RC models, never consumers were modelled using two approaches: a zero distribution (Three-part RC-het-det), and logistic distribution (Three-part RC-het-prob); heteroscedasticity using an exponential distribution and right-skewness using generalized gamma distribution. The proposed models were compared with two-part RC model that ignores never consumers, and with methods that estimate intakes using FFQ and 24HR. The models were evaluated in a simulation study. With NHANES data, mean increase in the mercury level (in $\mu \mathrm{g}/\mathrm{L}$) was 1.20 using FFQ-method, 0.4 using 24HR-method, 1.87 using two-part RC and 2.02 using three-part RC-het-prob method. The three-part RC estimated the association with the least bias in the simulation study.

摘要

暴露测量误差 (ME) 偏向暴露-结果关联。用于调整 ME 的回归校准 (RC) 响应中使用的校准膳食摄入数据通常是右偏的、异方差的并且具有过多的零。我们提出了三部分 RC 模型来同时处理这些分布复杂性，同时校正鱼类摄入量中的 ME。我们应用了来自全国健康和营养检查调查 (NHANES) 的数据，在主要研究中使用食物频率问卷 (FFQ) 测量长期摄入量，在校准研究中使用 24 小时回忆 (24HR) 测量短期摄入量. 在三部分 RC 模型中，从未使用两种方法对消费者进行建模：零分布（三部分 RC-het-det）和逻辑分布（三部分 RC-het-prob）；使用指数分布的异方差性和使用广义伽马分布的右偏度。将提出的模型与忽略消费者的两部分 RC 模型以及使用 FFQ 和 24HR 估计摄入量的方法进行了比较。在模拟研究中对模型进行了评估。使用 NHANES 数据，平均增加的汞水平（在$\mu \mathrm{G}/\mathrm{大号}$) 使用 FFQ 方法为 1.20，使用 24HR 方法为 0.4，使用两部分 RC 为 1.87，使用三部分 RC-het-prob 方法为 2.02。由三部分组成的 RC 估计了模拟研究中偏差最小的关联。