Analysis of long series of longitudinal ordinal data using marginalized models

Lee, Keunbaik; Sohn, Insuk; Kim, Donguk

doi:10.1016/j.csda.2015.07.010

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…Therefore, the Generalized Estimating Equations (GEE) approach (Liang and Zeger, 1986) provides a convenient alternative to maximum likelihood estimation, especially for longitudinal categorical and ordinal data (Lumley, 1996;Parsons et al, 2006;Touloumis et al, 2013;Nooraee et al, 2014;Ditlhong et al, 2018;da Silva et al, 2019). Marginalized models, integrating the random effects in the likelihood functions to get the estimation for marginal regression parameters, have also been explored for longitudinal ordinal data (Lee and Daniels, 2008;Lee et al, 2016;Schildcrout et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Analysis of Longitudinal Ordinal Data Using Non-Identifiable Multivariate Probit Models

Zhang¹

2022

Journal of Mathematics and Statistics

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Multivariate probit models have been explored for analyzing longitudinal ordinal data. However, the inherent identification issue in multivariate probit models requires the covariance matrix of the underlying latent multivariate normal variables to be a correlation matrix and thus hinders the development of efficient Bayesian sampling methods. It is known that non-identifiable models may produce Markov Chain Monte Carlo (MCMC) samplers with better convergence and mixing than identifiable models. Therefore, we were motivated to construct a non-identifiable multivariate probit model and to develop efficient MCMC sampling algorithms. In comparison with the MCMC sampling algorithm based on the identifiable multivariate probit model, which requires a Metropolis-Hastings (MH) algorithm for sampling a correlation matrix, our proposed MCMC sampling algorithms based on the non-identifiable model circumvent an MH algorithm by a Gibbs sampler for sampling a covariance matrix and thus accelerate the MCMC convergence. We illustrate our proposed methods using simulation studies and two real data applications. Both the simulation studies and the real data applications show that constructing nonidentifiable models may improve the convergence of the MCMC algorithms compared with the identifiable models. The marginalization of the redundant parameters in the non-identifiable models should be considered in developing efficient MCMC sampling algorithms. This investigation shows that construction of non-identifiable models is valuable in developing MCMC sampling methods and illustrates advantages and disadvantages of construction of non-identifiable models to improve the convergence of the MCMC sampling components.

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Section: Introductionmentioning

confidence: 99%

Bayesian Analysis of Longitudinal Ordinal Data Using Non-Identifiable Multivariate Probit Models

Zhang¹

2022

Journal of Mathematics and Statistics

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Marginalized models for longitudinal count data

Lee

Joo

2019

Computational Statistics & Data Analysis

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Bayesian analysis of Turkish Income and Living Conditions data, using clustered longitudinal ordinal modelling with Bridge distributed random effects

Asar

2020

Statistical Modelling

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This article is motivated by the panel surveys, called Statistics on Income and Living Conditions (SILC), conducted annually on (randomly selected) country representative households to monitor EU 2020 aims on poverty reduction. We particularly consider the surveys conducted in Turkey within the scope of integration to the EU. Our main interests are on health aspects of economic and living conditions. The outcome is self-reported health that is clustered longitudinal ordinal, since repeated measures of it are nested within individuals and individuals are nested within families. Economic and living conditions have been measured through a number of individual- and family-level explanatory variables. The questions of interest are on the marginal relationships between the outcome and covariates that we address using a polytomous logistic regression with Bridge distributed random effects. This choice of distribution allows us to directly obtain marginal inferences in the presence of random effects. Widely used Normal distribution is also considered as the random effects distribution. Samples from the joint posterior densities of parameters and random effects are drawn using Markov Chain Monte Carlo. Interesting findings from the public health point of view are that differences were found between the subgroups of employment status, income level and panel year in terms of odds of reporting better health.

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Analysis of long series of longitudinal ordinal data using marginalized models

Cited by 3 publications

References 26 publications

Bayesian Analysis of Longitudinal Ordinal Data Using Non-Identifiable Multivariate Probit Models

Bayesian Analysis of Longitudinal Ordinal Data Using Non-Identifiable Multivariate Probit Models

Marginalized models for longitudinal count data

Bayesian analysis of Turkish Income and Living Conditions data, using clustered longitudinal ordinal modelling with Bridge distributed random effects

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