Statistical Modelling 21 (5) (2021), 405–427
Bayesian analysis of Turkish Income and Living Conditions data, using clustered longitudinal ordinal modelling with Bridge distributed random effects
Özgür Asar,
Department of Biostatistics and Medical Informatics,
Faculty of Medicine,
Acibadem Mehmet Ali Aydinlar University,
Istanbul,
Turkey.
e-mail: ozgur.asar@acibadem.edu.tr
Abstract:
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 t he 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.
Keywords:
bridge distribution, latent variables, multilevel data, repeated measures, self-reported
health
Downloads:
R codes, a simulated dataset and exemplary codes for data analysis are available in R package
mixed3
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