Group-based trajectory modeling (GBTM) identifies groups of individuals following similar trajectories of one or more repeated measures. The categorical nature of GBTM is particularly well suited to clinical psychology and medicine, where patients are often classified into discrete diagnostic categories. This review highlights recent advances in GBTM and key capabilities that remain underappreciated in clinical research. These include accounting for nonrandom subject attrition, joint trajectory and multitrajectory modeling, the addition of the beta distribution to modeling options, associating trajectories with future outcomes, and estimating the probability of future outcomes. Also discussed is an approach to selecting the number of trajectory groups.

中文翻译：

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基于组的轨迹建模在临床研究中的最新进展


基于组的轨迹建模 （GBTM） 识别遵循一个或多个重复测量的相似轨迹的个体组。GBTM 的分类性质特别适合临床心理学和医学，在这些领域中，患者通常被分为离散的诊断类别。本综述重点介绍了 GBTM 的最新进展和临床研究中仍未被充分重视的关键能力。这些包括考虑非随机受试者流失、联合轨迹和多轨迹建模、将 beta 分布添加到建模选项中、将轨迹与未来结果相关联以及估计未来结果的概率。还讨论了一种选择轨迹组数量的方法。