Bayesian modelling of statistical region- and family-level clustered ordinal outcome data from Turkey
Ozgur Asar

TL;DR
This paper develops a Bayesian polytomous logistic regression model with random effects for three-level ordinal data, applied to Turkish health survey data, revealing regional and demographic health disparities.
Contribution
It introduces a Bayesian approach using Bridge distribution for random effects in multilevel ordinal data analysis, enabling marginal interpretation of covariates.
Findings
Unemployed individuals are 19% more likely to report poorer health.
Rural Aegean has the lowest probability of reporting poorer health.
Both region- and family-level variabilities are significant in modeling health outcomes.
Abstract
This study is concerned with the analysis of three-level ordinal outcome data with polytomous logistic regression in the presence of random-effects. It is assumed that the random-effects follow a Bridge distribution for the logit link, which allows one to obtain marginal interpretations of the regression coefficients. The data are obtained from the Turkish Income and Living Conditions Study, where the outcome variable is self-rated health (SRH), which is ordinal in nature. The analysis of these data is to compare covariate sub-groups and draw region- and family-level inferences in terms of SRH. Parameters and random-effects are sampled from the joint posterior densities following a Bayesian paradigm. Three criteria are used for model selection: Watenable information criterion, log pseudo marginal likelihood, and deviance information criterion. All three suggest that we need to account…
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Taxonomy
TopicsHealth disparities and outcomes · Global Health Care Issues · Income, Poverty, and Inequality
