Green Michael J
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, 200 Renfield Street, Glasgow, G2 3QB, United Kingdom.
J Clin Epidemiol. 2014 Oct;67(10):1157-62. doi: 10.1016/j.jclinepi.2014.05.005. Epub 2014 Jun 20.
Latent class methods are increasingly being used in analysis of developmental trajectories. A recent simulation study by Twisk and Hoekstra (2012) suggested caution in use of these methods because they failed to accurately identify developmental patterns that had been artificially imposed on a real data set. This article tests whether existing developmental patterns within the data set used might have obscured the imposed patterns.
Data were simulated to match the latent class pattern in the previous article, but with varying levels of randomly generated variance, rather than variance carried over from a real data set. Latent class analysis (LCA) was then used to see if the latent class structure could be accurately identified.
LCA performed very well at identifying the simulated latent class structure, even when the level of variance was similar to that reported in the previous study, although misclassification began to be more problematic with considerably higher levels of variance.
The failure of LCA to replicate the imposed patterns in the previous study may have been because it was sensitive enough to detect residual patterns of population heterogeneity within the altered data. LCA performs well at classifying developmental trajectories.
潜在类别方法在发展轨迹分析中的应用日益广泛。Twisk和Hoekstra（2012年）最近的一项模拟研究表明，使用这些方法时需谨慎，因为它们未能准确识别人为施加于真实数据集上的发展模式。本文检验了所用数据集中现有的发展模式是否可能掩盖了施加的模式。
模拟数据以匹配上一篇文章中的潜在类别模式，但具有不同水平的随机生成方差，而非从真实数据集继承的方差。然后使用潜在类别分析（LCA）来查看潜在类别结构是否能被准确识别。
即使方差水平与上一项研究报告的相似，LCA在识别模拟的潜在类别结构方面表现非常出色，尽管在方差水平显著更高时，错误分类开始变得更成问题。
LCA未能在前一项研究中复制施加的模式，可能是因为它足够敏感，能够检测到改变后数据中人群异质性的残余模式。LCA在分类发展轨迹方面表现良好。
