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

UMMS Affiliation

Department of Quantitative Health Sciences

Publication Date


Document Type



Longitudinal Studies; Pattern Recognition, Automated; Fuzzy Logic; Models, Statistical


Bioinformatics | Biostatistics | Epidemiology | Health Services Research


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.

DOI of Published Version



Int J Inf Technol Decis Mak. 2009 Sep 1;8(3):491-513. Link to article on publisher's site

Journal/Book/Conference Title

International journal of information technology and decision making

PubMed ID


Related Resources

Link to Article in PubMed