Freedman Laurence S, Midthune Douglas, Carroll Raymond J, Commins John M, Arab Lenore, Baer David J, Moler James E, Moshfegh Alanna J, Neuhouser Marian L, Prentice Ross L, Rhodes Donna, Spiegelman Donna, Subar Amy F, Tinker Lesley F, Willett Walter, Kipnis Victor
From the aInformation Management Services, Inc., Rockville, MD; bNational Cancer Institute, Bethesda, MD; cUniversity Texas A&M, College Station, TX; dUniversity of California Los Angeles, Los Angeles, CA; eUnited States Department of Agriculture, Beltsville, MD; fFred Hutchinson Cancer Research Center, Seattle, WA; and gHarvard School of Public Health, Boston, MA.
Epidemiology. 2015 Nov;26(6):925-33. doi: 10.1097/EDE.0000000000000377.
Most statistical methods that adjust analyses for dietary measurement error treat an individual's usual intake as a fixed quantity. However, usual intake, if defined as average intake over a few months, varies over time. We describe a model that accounts for such variation and for the proximity of biomarker measurements to self-reports within the framework of a meta-analysis, and apply it to the analysis of data on energy, protein, potassium, and sodium from a set of five large validation studies of dietary self-report instruments using recovery biomarkers as reference instruments. We show that this time-varying usual intake model fits the data better than the fixed usual intake assumption. Using this model, we estimated attenuation factors and correlations with true longer-term usual intake for single and multiple 24-hour dietary recalls (24HRs) and food frequency questionnaires (FFQs) and compared them with those obtained under the "fixed" method. Compared with the fixed method, the estimates using the time-varying model showed slightly larger values of the attenuation factor and correlation coefficient for FFQs and smaller values for 24HRs. In some cases, the difference between the fixed method estimate and the new estimate for multiple 24HRs was substantial. With the new method, while four 24HRs had higher estimated correlations with truth than a single FFQ for absolute intakes of protein, potassium, and sodium, for densities the correlations were approximately equal. Accounting for the time element in dietary validation is potentially important, and points toward the need for longer-term validation studies.
大多数针对膳食测量误差调整分析的统计方法都将个体的通常摄入量视为一个固定量。然而，如果将通常摄入量定义为几个月内的平均摄入量，那么它会随时间变化。我们描述了一种模型，该模型在荟萃分析框架内考虑了这种变化以及生物标志物测量值与自我报告的接近程度，并将其应用于对一组五项大型膳食自我报告工具验证研究中的能量、蛋白质、钾和钠数据的分析，这些研究使用恢复性生物标志物作为参考工具。我们表明，这种随时间变化的通常摄入量模型比固定的通常摄入量假设更适合数据。使用该模型，我们估计了单次和多次24小时膳食回忆（24HR）及食物频率问卷（FFQ）的衰减因子以及与真实长期通常摄入量的相关性，并将它们与在“固定”方法下获得的结果进行比较。与固定方法相比，使用随时间变化模型的估计显示，FFQ的衰减因子和相关系数值略大，而24HR的值略小。在某些情况下，多次24HR的固定方法估计值与新估计值之间的差异很大。采用新方法时，对于蛋白质、钾和钠的绝对摄入量，四次24HR与真实值的估计相关性高于单次FFQ，但对于密度而言，相关性大致相等。在膳食验证中考虑时间因素可能很重要，这表明需要进行长期验证研究。
