Medicine and Health Sciences Commons^™

Pattern Recognition Of Longitudinal Trial Data With Nonignorable Missingness: An Empirical Case Study, Julia Hua Fang, Kimberly A. Espy, Maria L. Rizzo, Christian Stopp, Sandra A. Wiebe, Walter W. Stroup

Developmental Cognitive Neuroscience Laboratory: Faculty and Staff Publications

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 …