PATTERN RECOGNITION OF LONGITUDINAL TRIAL DATA WITH NONIGNORABLE MISSINGNESS: AN EMPIRICAL CASE STUDY
- 1 September 2009
- journal article
- research article
- Published by World Scientific Pub Co Pte Ltd in International Journal of Information Technology & Decision Making
- Vol. 08 (03) , 491-513
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.Keywords
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