An overview of mixture modelling for latent evolutions in longitudinal data: Modelling approaches, fit statistics and software
An overview of mixture modelling for latent evolutions in longitudinal data: Modelling approaches, fit statistics and software
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DOI：
10.1016/j.alcr.2019.100323
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发表时间：
2020-03-01
影响因子：
3.4
通讯作者：
van Breukelen, Gerard J. P.
中科院分区：
文献类型：
--
作者：
van der Nest, Gavin;Passos, Valeria Lima;van Breukelen, Gerard J. P.
有限混合模型(FMM)用于纵向重复测量数据的分析正变得越来越流行。FMMS有助于识别遵循类似时间发展路径的潜在类别。本文旨在通过介绍各种可用的技术来解决新接触这些方法的从业者所经历的困惑，其中包括对它们的相互关联性和适用性的概述。我们的重点将集中在常用的基于模型的方法上，包括潜在类增长分析(LCGA)、基于组的轨迹模型(GBTM)和增长混合模型(GMM)。我们讨论了模型选择的标准，强调了模型拟合中经常遇到的挑战和悬而未决的问题，展示了模型在软件中的可用性，并用一个应用实例说明了模型选择策略。
The use of finite mixture modelling (FMM) is becoming increasingly popular for the analysis of longitudinal repeated measures data. FMMs assist in identifying latent classes following similar paths of temporal development. This paper aims to address the confusion experienced by practitioners new to these methods by introducing the various available techniques, which includes an overview of their interrelatedness and applicability. Our focus will be on the commonly used model-based approaches which comprise latent class growth analysis (LCGA), group-based trajectory models (GBTM), and growth mixture modelling (GMM). We discuss criteria for model selection, highlight often encountered challenges and unresolved issues in model fitting, showcase model availability in software, and illustrate a model selection strategy using an applied example.