The use of longitudinal finite mixture models such as group-based trajectory modeling has seen a sharp increase during the last decades in the medical literature. However, these methods have been criticized especially because of the data-driven modelling process which involves statistical decision-making. In this paper, we propose an approach that uses bootstrap to sample observations with replacement from the original data to validate the number of groups identified and to quantify the uncertainty in the number of groups. The method allows investigating the statistical validity and the uncertainty of the groups identified in the original data by checking if the same solution is also found across the bootstrap samples. In a simulation study, we examined whether the bootstrap-estimated variability in the number of groups reflected the replication-wise variability. We also compared the replication-wise variability to the Bayesian posterior probability. We evaluated the ability of three commonly used adequacy criteria (average posterior probability, odds of correct classification and relative entropy) to identify uncertainty in the number of groups. Finally, we illustrated the proposed approach using data from the Quebec Integrated Chronic Disease Surveillance System to identify longitudinal medication patterns between 2015 and 2018 in older adults with diabetes.
翻译:在过去几十年里,医学文献中,使用长纵向有限混合物模型,如基于团体的轨迹模型等,在医学文献中出现急剧增长;然而,这些方法受到批评,特别是因为数据驱动的模型制作过程涉及统计决策;在本文件中,我们建议采用一种方法,利用陷阱对观测进行抽样,取而代之,取而代之的是原始数据,以验证所查明的团体数目,并量化组数的不确定性;这种方法有助于调查原始数据中查明的团体的统计有效性和不确定性,方法是通过检查是否在靴套样本中也发现了同样的解决方案;在一项模拟研究中,我们研究了组数中的靴状估计变异性是否反映了复制性的变异性;我们还将复制性的变异性与巴伊西亚的事后概率作了比较;我们评估了三种常用的适足性标准(平均远地点概率、正确分类的概率和相对的诱变性)的能力,以确定群数的不确定性;最后,我们用魁北克综合慢性疾病监测系统的数据说明了拟议采用的方法,以确定2015年至2018年年糖尿病老年人的长度药物模式。