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 journalArticle

17 Citations (Scopus)

Abstract

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
Volume8
Issue number3
DOIs
StatePublished - Sep 2009
Externally publishedYes

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Pattern recognition
Fuzzy clustering
Computational efficiency
Data mining
Trajectories

Keywords

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

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

Cite this

Pattern recognition of longitudinal trial data with nonignorable missingness. / Fang, Hua; Espy, Kimberly Andrews; Rizzo, Maria L.; Stopp, Christian; Wiebe, Sandra A.; Stroup, Walter W.

In: International Journal of Information Technology and Decision Making, Vol. 8, No. 3, 09.2009, p. 491-513.

Research output: Contribution to journalArticle

Fang, Hua ; Espy, Kimberly Andrews ; Rizzo, Maria L. ; Stopp, Christian ; Wiebe, Sandra A. ; Stroup, Walter W. / Pattern recognition of longitudinal trial data with nonignorable missingness. In: International Journal of Information Technology and Decision Making. 2009 ; Vol. 8, No. 3. pp. 491-513.
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