Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center.
Department of Pediatrics, University of Cincinnati College of Medicine.
J Pediatr Psychol. 2021 Feb 19;46(2):179-188. doi: 10.1093/jpepsy/jsab010.
This article guides researchers through the process of specifying, troubleshooting, evaluating, and interpreting latent growth mixture models.
Latent growth mixture models are conducted with small example dataset of N = 117 pediatric patients using Mplus software.
The example and data show how to select a solution, here a 3-class solution. We also present information on two methods for incorporating covariates into these models.
Many studies in pediatric psychology seek to understand how an outcome changes over time. Mixed models or latent growth models estimate a single average trajectory estimate and an overall estimate of the individual variability, but this may mask other patterns of change shared by some participants. Unexplored variation in longitudinal data means that researchers can miss critical information about the trajectories of subgroups of individuals that could have important clinical implications about how one assess, treats, and manages subsets of individuals. Latent growth mixture modeling is a method for uncovering subgroups (or "classes") of individuals with shared trajectories that differ from the average trajectory.
本文指导研究人员完成指定、故障排除、评估和解释潜在增长混合模型的过程。
使用 Mplus 软件对 N=117 名儿科患者的小型示例数据集进行潜在增长混合模型分析。
示例和数据展示了如何选择解决方案，这里是 3 类解决方案。我们还介绍了将协变量纳入这些模型的两种方法的信息。
儿科心理学中的许多研究都试图了解结果随时间的变化。混合模型或潜在增长模型估计单个平均轨迹估计值和个体变异性的总体估计值，但这可能掩盖了某些参与者共享的其他变化模式。纵向数据中未被探索的变化意味着研究人员可能会错过关于个体轨迹的亚组的关键信息，这可能对如何评估、治疗和管理个体的亚组具有重要的临床意义。潜在增长混合模型是一种用于发现具有相似轨迹但与平均轨迹不同的个体亚组（或“类”）的方法。
