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

Authors

Julia Hua Fang, University of NebraskaFollow
Kimberly A. Espy, University of Nebraska-LincolnFollow
Maria L. Rizzo, Bowling Green State University,Follow
Christian Stopp, University of Nebraska-LincolnFollow
Sandra A. Wiebe, University of Nebraska-LincolnFollow
Walter W. Stroup, University of NebraskaFollow

Date of this Version

2009

Comments

Published in International Journal of Information Technology & Decision Making, Vol. 8, No. 3 (2009), pp. 491–513. Copyright © 2009 World Scientific Publishing Company. Used by permission. http://www.worldscinet.com/ijitdm/ijitdm.shtml

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.