Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, 55454, USA.
Am J Phys Anthropol. 2013 Jan;150(1):58-67. doi: 10.1002/ajpa.22128.
This article demonstrates the use of mixed effects models for characterizing individual and sample average growth curves based on serial anthropometric data. These models are advancement over conventional general linear regression because they effectively handle the hierarchical nature of serial growth data. Using body weight data on 70 infants in the Born in Bradford study, we demonstrate how a mixed effects model provides a better fit than a conventional regression model. Further, we demonstrate how mixed effects models can be used to explore the influence of environmental factors on the sample average growth curve. Analyzing data from 183 infant boys (aged 3-15 months) from rural South India, we show how maternal education shapes infant growth patterns as early as within the first 6 months of life. The presented analyses highlight the utility of mixed effects models for analyzing serial growth data because they allow researchers to simultaneously predict individual curves, estimate sample average curves, and investigate the effects of environmental exposure variables.
本文展示了如何使用混合效应模型来描述基于序列人体测量数据的个体和样本平均生长曲线。这些模型是对传统的一般线性回归的改进，因为它们有效地处理了序列生长数据的层次性质。我们使用布拉德福德出生研究中 70 名婴儿的体重数据，展示了混合效应模型如何比传统回归模型提供更好的拟合。此外，我们还展示了如何使用混合效应模型来探索环境因素对样本平均生长曲线的影响。我们分析了来自印度南部农村的 183 名男婴（3-15 个月龄）的数据，结果表明，母亲的教育水平早在婴儿生命的头 6 个月内就影响了婴儿的生长模式。所呈现的分析强调了混合效应模型在分析序列生长数据方面的实用性，因为它们允许研究人员同时预测个体曲线、估计样本平均曲线，并研究环境暴露变量的影响。
