Name
Title | ||
|---|---|---|
Pattern Recognition Of Longitudinal Trial Data With Nonignorable Missingness: An Empirical Case Study |
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1142/S0219622009003508 | INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING |
Keywords | Field | DocType |
Nonmissing at random, intermittent missing, growth pattern recognition, parallel mixture model, fuzzy clustering | Fuzzy clustering,Data mining,Text mining,Pattern recognition,Computer science,Artificial intelligence,Missing data,Mixture model,Machine learning,Generality | Journal |
Volume | Issue | ISSN |
8 | 3 | 0219-6220 |
Citations | PageRank | References |
8 | 0.66 | 19 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hua Fang | 1 | 343 | 32.48 |
| Kimberly Andrews Espy | 2 | 22 | 1.94 |
| Maria L. Rizzo | 3 | 107 | 9.54 |
| Christian Stopp | 4 | 8 | 0.66 |
| Sandra A Wiebe | 5 | 8 | 0.66 |
| Walter W Stroup | 6 | 8 | 0.66 |