Journal
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
Volume 8, Issue 3, Pages 491-513Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0219622009003508
Keywords
Nonmissing at random; intermittent missing; growth pattern recognition; parallel mixture model; fuzzy clustering
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Funding
- National Institute on Drug Abuse [R01 DA014661]
- National Institute of Mental Health [R01 MH065668]
- National Institute of Child Health and Development [P01 HD038051, R01 HD050309]
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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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