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Log-linear Rasch-type models for repeated categorical data with a psychobiological application

Hatzinger, Reinhold and Katzenbeisser, Walter (2008) Log-linear Rasch-type models for repeated categorical data with a psychobiological application. Research Report Series / Department of Statistics and Mathematics, 69. Department of Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna.

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Abstract

The purpose of this paper is to generalize regression models for repeated categorical data based on maximizing a conditional likelihood. Some existing methods, such as those proposed by Duncan (1985), Fischer (1989), and Agresti (1993, and 1997) are special cases of this latent variable approach, used to account for dependencies in clustered observations. The generalization concerns the incorporation of rather general data structures such as subject-specific time-dependent covariates, a variable number of observations per subject and time periods of arbitrary length in order to evaluate treatment effects on a categorical response variable via a linear parameterization. The response may be polytomous, ordinal or dichotomous. The main tool is the log-linear representation of appropriately parameterized Rasch-type models, which can be fitted using standard software, e.g., R. The proposed method is applied to data from a psychiatric study on the evaluation of psychobiological variables in the therapy of depression. The effects of plasma levels of the antidepressant drug Clomipramine and neuroendocrinological variables on the presence or absence of anxiety symptoms in 45 female patients are analyzed. The individual measurements of the time dependent variables were recorded on 2 to 11 occasions. The findings show that certain combinations of the variables investigated are favorable for the treatment outcome. (author´s abstract)

Item Type: Paper
Keywords: latent variables / Rasch model / time-dependent covariates / conditional maximum likelihood / log-linear models / quasi-symmetry / R
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 21 Jul 2008 14:44
Last Modified: 29 Jun 2015 03:20
URI: http://epub.wu.ac.at/id/eprint/126

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