Methods for identifying meaningful growth patterns of longitudinal trial data with both nonignorable intermittent and drop-out missingness are rare. In this study, a combined approach with statistical and data mining techniques is utilized to address the nonignorable missing data issue in growth pattern recognition. First, a parallel mixture model is proposed to model the nonignorable missing information from a real-world patient-oriented study and concurrently to estimate the growth trajectories of participants. Then, based on individual growth parameter estimates and their auxiliary feature attributes, a fuzzy clustering method is incorporated to identify the growth patterns. This case study demonstrates that the combined multi-step approach can achieve both statistical generality and computational efficiency for growth pattern recognition in longitudinal studies with nonignorable missing data.

中文翻译：

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具有不可忽略缺失的纵向试验数据的模式识别：实证案例研究


识别具有不可忽略的间歇性和退出缺失的纵向试验数据的有意义的增长模式的方法很少。在本研究中，采用统计和数据挖掘技术相结合的方法来解决生长模式识别中不可忽略的缺失数据问题。首先，提出了一个并行混合模型来模拟现实世界中以患者为导向的研究中不可忽略的缺失信息，同时估计参与者的成长轨迹。然后，基于个体生长参数估计及其辅助特征属性，采用模糊聚类方法来识别生长模式。本案例研究表明，组合的多步骤方法可以在具有不可忽略的缺失数据的纵向研究中实现生长模式识别的统计通用性和计算效率。