Pattern recognition of longitudinal trial data with nonignorable missingness:

Hua Fang, Kimberly Andrews Espy, Maria L. Rizzo, Christian Stopp, Sandra A. Wiebe, Walter W. Stroup

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)491-513
Number of pages23
JournalInternational Journal of Information Technology and Decision Making
Issue number3
StatePublished - Sep 2009


  • Fuzzy clustering
  • Growth pattern recognition
  • Intermittent missing
  • Nonmissing at random
  • Parallel mixture model

ASJC Scopus subject areas

  • Computer Science (miscellaneous)


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