Latent growth curve modeling for longitudinal ordinal responses with applications
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AbstractIn this paper we consider the use of the latent growth curve model to analyze longitudinal ordinal categorical data that Involve measurements at different time points By operating on the assumption that the ordinal response variables at different time points are related to normally distributed underlying continuous variables and by further modeling these underlying continuous variables for different time points with the latent growth curve model we achieve a comprehensive and flexible model with straightforward interpretations and a variety of applications We discuss the applications of the model in treatment comparisons and in the analysis of the covanate effects Moreover one prominent advantage of the model lies in its ability to address possible differences in the initial conditions for the subjects who take part in different treatments Making use of this property we also develop a new method to test the equivalence of two treatments that involve ordinal responses obtained at two different time points A real data set is used to illustrate the applicability and practicality of the proposed approach (C) 2010 Elsevier B V All rights reserved
All Author(s) ListLu TY, Poon WY, Tsang YF
Journal nameComputational Statistics and Data Analysis
Volume Number55
Issue Number3
Pages1488 - 1497
LanguagesEnglish-United Kingdom
KeywordsCovariates; Equivalence test; Latent growth curve model; Ordinal categorical responses; Treatment comparison
Web of Science Subject CategoriesComputer Science; Computer Science, Interdisciplinary Applications; COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS; Mathematics; Statistics & Probability; STATISTICS & PROBABILITY

Last updated on 2021-14-01 at 23:27