Sijbrandij Jitske J, Hoekstra Tialda, Almansa Josué, Reijneveld Sijmen A, Bültmann Ute
University of Groningen, University Medical Center Groningen, Department of Health Sciences, Community and Occupational Medicine, Groningen, the Netherlands.
Adv Life Course Res. 2019 Dec;42:100288. doi: 10.1016/j.alcr.2019.04.018. Epub 2019 Apr 27.
Several statistical methods are available to identify developmental trajectory classes, but it is unclear which method is most suitable. The aim of this study was to determine whether latent class analysis, latent class growth analysis or growth mixture modeling was most appropriate for identifying developmental trajectory classes.
We compared the three methods in a simulation study in several scenarios, which varied regarding e.g. sample size and degree of separation between classes. The simulation study was replicated with a real data example concerning anxiety/depression symptoms measured over 6 time points in the Tracking Adolescent Individuals' Lives Survey (TRAILS, N = 2227).
Growth mixture modeling was least biased or equally biased compared to latent class analysis and latent class growth analysis in all scenarios. In TRAILS, the shapes of the trajectories were rather similar over the three methods, but class sizes differed slightly. A 4-class growth mixture model performed best, based on several fit indices, interpretability and clinical relevance.
Growth mixture modeling seems most suitable to identify developmental trajectory classes.
有几种统计方法可用于识别发育轨迹类别，但尚不清楚哪种方法最为合适。本研究的目的是确定潜在类别分析、潜在类别增长分析或增长混合模型在识别发育轨迹类别方面是否最为适用。
我们在一项模拟研究中，于若干情景下比较了这三种方法，这些情景在例如样本量和类别间分离程度等方面存在差异。以追踪青少年个体生活调查（TRAILS，N = 2227）中在6个时间点测量的焦虑/抑郁症状的真实数据示例对模拟研究进行了重复。
在所有情景中，与潜在类别分析和潜在类别增长分析相比，增长混合模型的偏差最小或偏差相当。在TRAILS中，三种方法得到的轨迹形状相当相似，但类别大小略有不同。基于若干拟合指数、可解释性和临床相关性，一个4类别增长混合模型表现最佳。
增长混合模型似乎最适合用于识别发育轨迹类别。
