Pattern Recognition of Longitudinal Trial Data with Nonignorable Missingness: An Empirical Case Study
Department of Quantitative Health Sciences
Medical Subject Headings
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.
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Citation: Int J Inf Technol Decis Mak. 2009 Sep 1;8(3):491-513. Link to article on publisher's site