Carroll R J, Freedman L S, Kipnis V
Department of Statistics, Texas A&M University, College Station 77843, USA.
Adv Exp Med Biol. 1998;445:139-45. doi: 10.1007/978-1-4899-1959-5_9.
This chapter reviews work of Carroll, Freedman, Kipnis, and Li (1998) on the statistical analysis of the relationship between dietary intake and health outcomes. In the area of nutritional epidemiology, there is some evidence from biomarker studies that the usual statistical model for dietary measurements may break down due to two causes: (a) systematic biases depending on a person's body mass index; and (b) an additional random component of bias, so that the error structure is the same as a one-way random effects model. We investigate this problem, in the context of (1) the estimation of the distribution of usual nutrient intake; (2) estimating the correlation between a nutrient instrument and usual nutrient intake; and (3) estimating the true relative risk from an estimated relative risk using the error-prone covariate. While systematic bias due to body mass index appears to have little effect, the additional random effect in the variance structure is shown to have a potentially important impact on overall results, both on corrections for relative risk estimates and in estimating the distribution usual of nutrient intake. Our results point to a need for new experiments aimed at estimation of a crucial parameter.
本章回顾了卡罗尔、弗里德曼、基普尼斯和李（1998年）关于饮食摄入量与健康结果之间关系的统计分析工作。在营养流行病学领域，生物标志物研究有一些证据表明，饮食测量的常用统计模型可能因两个原因而失效：（a）取决于一个人体重指数的系统偏差；（b）偏差的额外随机成分，因此误差结构与单向随机效应模型相同。我们在以下背景下研究这个问题：（1）估计通常营养素摄入量的分布；（2）估计营养素测量工具与通常营养素摄入量之间的相关性；（3）使用容易出错的协变量从估计的相对风险中估计真实的相对风险。虽然体重指数导致的系统偏差似乎影响不大，但方差结构中的额外随机效应被证明对总体结果有潜在的重要影响，无论是对相对风险估计的校正，还是在估计通常营养素摄入量的分布方面。我们的结果表明需要开展新的实验来估计一个关键参数。
