Longitudinal nominal data analysis using marginalized models

Authors:
Keunbaik Lee;Donald Mercante
Affiliations:
Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA;Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA
Venue:
Computational Statistics & Data Analysis
Year:
2010

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Mining patterns from longitudinal studies

ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II

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Abstract

Recently, marginalized transition models have become popular for the analysis of longitudinal data. Heagerty (2002) and Lee and Daniels (2007) proposed marginalized transition models for the analysis of longitudinal binary data and ordinal data, respectively. In this paper, we extend their work to accommodate longitudinal nominal data using a Markovian dependence structure. A Fisher-scoring algorithm is developed for estimation. Methods are illustrated with a real dataset and are compared with other standard methods.