Siddique Juned, Daniels Michael J, Carroll Raymond J, Raghunathan Trivellore E, Stuart Elizabeth A, Freedman Laurence S
Department of Preventive Medicine, Northwestern University, Chicago, Illinois.
Department of Statistics, University of Florida, Gainesville, Florida.
Biometrics. 2019 Sep;75(3):927-937. doi: 10.1111/biom.13044. Epub 2019 Apr 6.
In lifestyle intervention trials, where the goal is to change a participant's weight or modify their eating behavior, self-reported diet is a longitudinal outcome variable that is subject to measurement error. We propose a statistical framework for correcting for measurement error in longitudinal self-reported dietary data by combining intervention data with auxiliary data from an external biomarker validation study where both self-reported and recovery biomarkers of dietary intake are available. In this setting, dietary intake measured without error in the intervention trial is missing data and multiple imputation is used to fill in the missing measurements. Since most validation studies are cross-sectional, they do not contain information on whether the nature of the measurement error changes over time or differs between treatment and control groups. We use sensitivity analyses to address the influence of these unverifiable assumptions involving the measurement error process and how they affect inferences regarding the effect of treatment. We apply our methods to self-reported sodium intake from the PREMIER study, a multi-component lifestyle intervention trial.
在生活方式干预试验中，目标是改变参与者的体重或调整其饮食行为，自我报告的饮食是一个纵向结果变量，容易受到测量误差的影响。我们提出了一个统计框架，通过将干预数据与来自外部生物标志物验证研究的辅助数据相结合，来校正纵向自我报告饮食数据中的测量误差，在该研究中，饮食摄入量的自我报告生物标志物和恢复生物标志物均可获得。在这种情况下，干预试验中无误差测量的饮食摄入量是缺失数据，采用多重填补法来填补缺失的测量值。由于大多数验证研究都是横断面研究，它们不包含关于测量误差的性质是否随时间变化或在治疗组和对照组之间是否不同的信息。我们使用敏感性分析来解决这些涉及测量误差过程的不可验证假设的影响，以及它们如何影响关于治疗效果的推断。我们将我们的方法应用于来自PREMIER研究的自我报告钠摄入量，这是一项多成分生活方式干预试验。
