Longitudinal nominal data analysis using marginalized models

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.