Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Université de Montréal, Montréal, QC, Canada.
Department of Social and Preventive Medicine, Université de Montréal, Montréal, QC, Canada.
BMC Med Res Methodol. 2022 Jul 14;22(1):194. doi: 10.1186/s12874-022-01622-9.
Group-based trajectory modelling (GBTM) is increasingly used to identify subgroups of individuals with similar patterns. In this paper, we use simulated and real-life data to illustrate that GBTM is susceptible to generating spurious findings in some circumstances.
Six plausible scenarios, two of which mimicked published analyses, were simulated. Models with 1 to 10 trajectory subgroups were estimated and the model that minimized the Bayes criterion was selected. For each scenario, we assessed whether the method identified the correct number of trajectories, the correct shapes of the trajectories, and the mean number of participants of each trajectory subgroup. The performance of the average posterior probabilities, relative entropy and mismatch criteria to assess classification adequacy were compared.
Among the six scenarios, the correct number of trajectories was identified in two, the correct shapes in four and the mean number of participants of each trajectory subgroup in only one. Relative entropy and mismatch outperformed the average posterior probability in detecting spurious trajectories.
Researchers should be aware that GBTM can generate spurious findings, especially when the average posterior probability is used as the sole criterion to evaluate model fit. Several model adequacy criteria should be used to assess classification adequacy.
基于群组的轨迹建模（GBTM）越来越多地用于识别具有相似模式的个体亚组。在本文中，我们使用模拟和真实数据来说明在某些情况下 GBTM 容易产生虚假发现。
模拟了六个合理的情况，其中两个模拟了已发表的分析。估计了具有 1 到 10 个轨迹亚组的模型，并选择了最小化贝叶斯准则的模型。对于每个场景，我们评估了该方法是否识别了正确的轨迹数量、轨迹的正确形状以及每个轨迹亚组的平均参与者数量。比较了平均后验概率、相对熵和不匹配标准来评估分类充分性的性能。
在六个场景中，有两个正确识别了轨迹数量，四个正确识别了轨迹形状，只有一个正确识别了每个轨迹亚组的平均参与者数量。相对熵和不匹配标准在检测虚假轨迹方面优于平均后验概率。
研究人员应该意识到，GBTM 可能会产生虚假发现，尤其是当平均后验概率被用作评估模型拟合度的唯一标准时。应该使用多个模型充分性标准来评估分类充分性。
