Individual-level longitudinal data on biological, behavioural, and social dimensions are becoming increasingly available. Typically, these data are analysed using mixed effects models, with the result summarised in terms of an average trajectory plus measures of the individual variations around this average. However, public health investigations would benefit from finer modelling of these individual variations which identify not just one average trajectory, but several typical trajectories. If evidence of heterogeneity in the development of these variables is found, the role played by temporally preceding (explanatory) variables as well as the potential impact of differential trajectories may have on later outcomes is often of interest. A wide choice of methods for uncovering typical trajectories and relating them to precursors and later outcomes exists. However, despite their increasing use, no practical overview of these methods targeted at epidemiological applications exists. Hence we provide: (a) a review of the three most commonly used methods for the identification of latent trajectories (growth mixture models, latent class growth analysis, and longitudinal latent class analysis); and (b) recommendations for the identification and interpretation of these trajectories and of their relationship with other variables. For illustration, we use longitudinal data on childhood body mass index and parental reports of fussy eating, collected in the Avon Longitudinal Study of Parents and Children.

中文翻译：

---

识别纵向数据中的典型轨迹：建模策略和解释。


关于生物、行为和社会维度的个人层面的纵向数据变得越来越可用。通常，使用混合效应模型对这些数据进行分析，结果以平均轨迹加上围绕该平均值的个体变化的度量进行总结。然而，公共卫生调查将受益于对这些个体差异进行更精细的建模，该模型不仅可以识别一种平均轨迹，还可以识别几种典型的轨迹。如果发现这些变量的发展存在异质性的证据，那么先前的（解释性）变量所发挥的作用以及差异轨迹对以后结果可能产生的潜在影响通常会引起人们的兴趣。存在多种方法来揭示典型轨迹并将其与前兆和后来的结果联系起来。然而，尽管它们的使用越来越多，但针对流行病学应用的这些方法还没有实际概述。因此，我们提供：（a）对识别潜在轨迹的三种最常用方法（增长混合模型、潜在类增长分析和纵向潜在类分析）进行回顾； (b) 识别和解释这些轨迹及其与其他变量关系的建议。为了说明这一点，我们使用雅芳父母和儿童纵向研究中收集的儿童体重指数和父母挑食报告的纵向数据。