PATTERN RECOGNITION OF LONGITUDINAL TRIAL DATA WITH NONIGNORABLE MISSINGNESS: AN EMPIRICAL CASE STUDY

Hua Fang (), Kimberly Andrews Espy, Maria L. Rizzo, Christian Stopp, Sandra A. Wiebe and Walter W. Stroup
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Hua Fang: Office of Research, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
Kimberly Andrews Espy: Office of Research, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
Maria L. Rizzo: Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, Ohio 43403, USA
Christian Stopp: Office of Research, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
Sandra A. Wiebe: Office of Research, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
Walter W. Stroup: Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE 68588, USA

International Journal of Information Technology & Decision Making (IJITDM), 2009, vol. 08, issue 03, 491-513

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.

Keywords: Nonmissing at random; intermittent missing; growth pattern recognition; parallel mixture model; fuzzy clustering (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:08:y:2009:i:03:n:s0219622009003508

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DOI: 10.1142/S0219622009003508

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