A Marginalized Overdispersed Location Scale Model for Clustered Ordinal Data

Nasim Vahabi (), Anoshirvan Kazemnejad and Somnath Datta ()
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Nasim Vahabi: University of Florida, Florida
Anoshirvan Kazemnejad: Tarbiat Modares University
Somnath Datta: University of Florida, Florida

Sankhya B: The Indian Journal of Statistics, 2018, vol. 80, issue 1, No 2, 103-134

Abstract: Abstract Overdispersion and intra cluster correlation are two important issues in clustered categorical/ordinal data and failure to account for them can result in misleading inferences. Generalized estimating equations and mixed effects models are two common frameworks for analyzing clustered data which are recently combined and extended to the marginalized random effects model. The location scale models are a different extension of the mixed effects models that furthermore allow the variance to vary as a function of covariates. In this paper, we extend a marginalized location scale model for longitudinal ordinal responses by allowing a log-linear model for variance components that facilitates both population-averaged and subject-specific interpretations. We then extend the marginalized location scale model by incorporating an additional random term into the model to handle the overdispersion aspect of the data. We conduct extensive simulation studies to investigate the statistical properties of the maximum likelihood estimators of the model parameters. We illustrate this methodology using a dataset on a children’s growth failure.

Keywords: Longitudinal ordinal outcome; Overdispersion; Beta distribution; Location-scale model; Marginalized framework.; Primary 62F10; Secondary 62J12 (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s13571-018-0162-5

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