Alternative regression models to assess increase in childhood BMI.
Alternative regression models to assess increase in childhood BMI.
复制标题
替代回归模型，以评估儿童BMI的增加。
DOI：
10.1186/1471-2288-8-59
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发表时间：
2008-09-08
影响因子：
4
通讯作者：
Toschke, Andre M.
中科院分区：
文献类型：
--
作者：
Beyerlein, Andreas;Fahrmeir, Ludwig;Mansmann, Ulrich;Toschke, Andre M.
身体质量指数(BMI)数据通常具有偏态分布，而常用的统计建模方法，如简单线性回归或Logistic回归等都有局限性。比较了不同的回归方法，包括广义线性模型(GLMS)、分位数回归和位置、尺度和形状的广义加性模型(GAMLSS)，通过拟合优度和解释方法来预测儿童BMI。我们分析了2001年至2002年在德国巴伐利亚州参加入学健康检查的4967名儿童的数据。肥胖的危险因素有看电视、进餐次数、母乳喂养、孕期吸烟、母亲肥胖、父母社会阶层和出生后头2年体重增加。相对于广义Akaike信息标准，GAMLSS在估计风险因素对转换和未转换的BMI数据的影响方面表现出比普通GLMS更好的拟合。与GAMLSS相比，分位数回归允许对预先指定的分布分位数进行额外的解释，例如表示超重或肥胖的分位数。前两年看电视、母亲BMI和体重增加的变量是直接的，而在所研究的任何类型的模型中，进餐频率与身体成分呈显著负相关。相反，在GLM模型中，孕期吸烟不是直接的，母乳喂养和父母的社会阶层与身体成分没有负相关，但在GAMLSS模型中，部分在分位数回归模型中。GAMLSS和分位数回归模型可以估计危险因素特有的BMI百分位数曲线。GAMLSS和分位数回归似乎比常见的GLMS更适合于BMI数据的危险因素建模。
Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.
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影响因子：
5.7
作者：
Eilers, PHC;Marx, BD
通讯作者：
Marx, BD
影响因子：
2
作者：
Rigby, RA;Stasinopoulos, DM
通讯作者：
Stasinopoulos, DM
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作者：
Terry, Mary Beth;Wei, Ying;Esserman, Denise
通讯作者：
Esserman, Denise
影响因子：
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作者：
Toschke, Andre M.;Martin, Richard M.;Ness, Andrew R.
通讯作者：
Ness, Andrew R.
影响因子：
2.7
作者：
JOHNSON, NL
通讯作者：
JOHNSON, NL