Pattern Recognition of Longitudinal Trial Data with Nonignorable Missingness: An Empirical Case Study
Authors
Fang, HuaEspy, Kimberly Andrews
Rizzo, Maria L.
Stopp, Christian
Wiebe, Sandra A.
Stroup, Walter W.
UMass Chan Affiliations
Department of Quantitative Health SciencesDocument Type
Journal ArticlePublication Date
2010-03-26Keywords
Longitudinal StudiesPattern Recognition, Automated
Fuzzy Logic
Models, Statistical
Bioinformatics
Biostatistics
Epidemiology
Health Services Research
Metadata
Show full item recordAbstract
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.Source
Int J Inf Technol Decis Mak. 2009 Sep 1;8(3):491-513. Link to article on publisher's siteDOI
10.1142/S0219622009003508Permanent Link to this Item
http://hdl.handle.net/20.500.14038/47747PubMed ID
20336179Related Resources
Link to Article in PubMedae974a485f413a2113503eed53cd6c53
10.1142/S0219622009003508