Pattern Recognition of Longitudinal Trial Data with Nonignorable Missingness: An Empirical Case Study

Hua Fang, University of Massachusetts Medical School
Kimberly Andrews Espy, University of Nebraska-Lincoln
Maria L. Rizzo, Bowling Green State University
Christian Stopp, University of Nebraska-Lincoln
Sandra A. Wiebe, University of Nebraska-Lincoln
Walter W. Stroup, University of Nebraska-Lincoln

Abstract

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.