Comparing four methods to estimate usual intake distributions
Comparing four methods to estimate usual intake distributions
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DOI：
10.1038/ejcn.2011.93
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发表时间：
2011-07-01
影响因子：
4.7
通讯作者：
van 't Veer, P.
中科院分区：
文献类型：
--
作者：
Souverein, O. W.;Dekkers, A. L.;van 't Veer, P.
背景/目的：本文的目的是比较估算营养素和食物的通常摄入分布的方法。由于“真实的”通常摄入量分布在实践中并不为人所知，因此通过模拟研究以及应用欧洲食品消费验证（EFCOVAL）研究的数据进行比较，其中收集了两个24小时饮食召回（24- hdr）和食物频率数据。比较的方法是爱荷华州立大学方法（ISU）、国家癌症研究所方法（NCI）、多来源方法（MSM）和年龄调整饮食评估统计计划（SPADE）。受试者/方法：模拟数据采用不同的受试者数量(n)、不同的Box-Cox变换参数（lambda(BC)）和不同的人内和人间方差比（r(var)）来构建。用四种不同的方法对所有数据进行分析，得到估计的通常平均摄入量和选定的百分位数。此外，作为一种额外的“方法”，估计了2天的人内平均值。这五种方法在平均偏差方面进行比较，平均偏差计算为估计值与已知真值之差的平均值。EFCOVAL项目数据的应用包括营养素（即蛋白质、钾、蛋白质密度）和食物（即蔬菜、水果和鱼）的计算。结果：总体而言，ISU、NCI、MSM和SPADE方法的平均偏倚较小。然而，对于所有方法，随着样本量的减少、方差比的增加和偏离正态性的增加，平均偏差和偏差的变化都增加了。当r(var) = 9, lambda(BC) = 0, n = 1000时，使用NCI方法可以看到严重的平均偏差（特别是在第95百分位）。与NCI和SPADE方法相比，ISU方法和MSM方法的偏倚标准差略高，表明方法的不确定性较大。此外，尽管ISU、NCI和SPADE方法根据定义产生了单峰密度函数，但当样本量较小时，MSM产生了具有“峰值”的分布，因为人口的通常摄入量分布是基于估计的个人通常摄入量。对EFCOVAL数据的应用表明，所分析的三种营养素的百分位数和平均值的所有估计值都在5%以内。对于蔬菜、水果和鱼类，差异大于营养成分，但总体而言，样本平均值的估计是合理的。结论：所比较的四种方法似乎可以很好地估计营养素的通常摄入分布。然而，当一种营养物质具有很高的个人差异或具有高度偏态分布时，以及当样本量很小时，需要注意。由于这些方法提供了不同的功能，因此可能存在选择其中一种方法的实际原因。欧洲临床营养学杂志（2011）65,S92-S101；doi: 10.1038 / ejcn.2011.93
Background/Objectives: The aim of this paper was to compare methods to estimate usual intake distributions of nutrients and foods. As 'true' usual intake distributions are not known in practice, the comparison was carried out through a simulation study, as well as empirically, by application to data from the European Food Consumption Validation (EFCOVAL) Study in which two 24-h dietary recalls (24-HDRs) and food frequency data were collected. The methods being compared were the Iowa State University Method (ISU), National Cancer Institute Method (NCI), Multiple Source Method (MSM) and Statistical Program for Age-adjusted Dietary Assessment (SPADE).Subjects/Methods: Simulation data were constructed with varying numbers of subjects (n), different values for the Box-Cox transformation parameter (lambda(BC)) and different values for the ratio of the within-and between-person variance (r(var)). All data were analyzed with the four different methods and the estimated usual mean intake and selected percentiles were obtained. Moreover, the 2-day within-person mean was estimated as an additional 'method'. These five methods were compared in terms of the mean bias, which was calculated as the mean of the differences between the estimated value and the known true value. The application of data from the EFCOVAL Project included calculations of nutrients (that is, protein, potassium, protein density) and foods (that is, vegetables, fruit and fish).Results: Overall, the mean bias of the ISU, NCI, MSM and SPADE Methods was small. However, for all methods, the mean bias and the variation of the bias increased with smaller sample size, higher variance ratios and with more pronounced departures from normality. Seriousmean bias (especially in the 95th percentile) was seen using the NCI Method when r(var) = 9, lambda(BC) = 0 and n = 1000. The ISU Method and MSM showed a somewhat higher s.d. of the bias compared with NCI and SPADE Methods, indicating a larger method uncertainty. Furthermore, whereas the ISU, NCI and SPADE Methods produced unimodal density functions by definition, MSM produced distributions with 'peaks', when sample size was small, because of the fact that the population's usual intake distribution was based on estimated individual usual intakes. The application to the EFCOVAL data showed that all estimates of the percentiles and mean were within 5% of each other for the three nutrients analyzed. For vegetables, fruit and fish, the differences were larger than that for nutrients, but overall the sample mean was estimated reasonably.Conclusions: The four methods that were compared seem to provide good estimates of the usual intake distribution of nutrients. Nevertheless, care needs to be taken when a nutrient has a high within-person variation or has a highly skewed distribution, and when the sample size is small. As the methods offer different features, practical reasons may exist to prefer one method over the other. European Journal of Clinical Nutrition (2011) 65, S92-S101; doi:10.1038/ejcn.2011.93