Morrell Christopher H, Brant Larry J, Ferrucci Luigi
Gerontology Research Center, National Institute on Aging, 5600 Nathan Shock Drive, Baltimore, MD 21224, USA.
J Gerontol A Biol Sci Med Sci. 2009 Feb;64(2):215-22. doi: 10.1093/gerona/gln024. Epub 2009 Feb 5.
This article examines how different parameterizations of age and time in modeling observational longitudinal data can affect results.
When individuals of different ages at study entry are considered, it becomes necessary to distinguish between longitudinal and cross-sectional differences to overcome possible selection biases.
Various models were fitted using data from longitudinal studies with participants with different ages and different follow-up lengths. Decomposing age into two components-age at entry into the study (first age) and the longitudinal follow-up (time) compared with considering age alone-leads to different conclusions.
In general, models using both first age and time terms performed better, and these terms are usually necessary to correctly analyze longitudinal data.
本文探讨在对观察性纵向数据进行建模时，年龄和时间的不同参数化方式如何影响结果。
当考虑研究入组时不同年龄的个体时，有必要区分纵向差异和横断面差异，以克服可能的选择偏倚。
使用来自纵向研究的数据对不同年龄和不同随访时长的参与者拟合了各种模型。将年龄分解为两个组成部分——研究入组时的年龄（初始年龄）和纵向随访（时间），与仅考虑年龄相比，会得出不同的结论。
一般来说，同时使用初始年龄和时间项的模型表现更好，这些项通常是正确分析纵向数据所必需的。
