title: Bayesian generalized linear mixed modeling of Tuberculosis using informative priors.
abstract: TB is rated as one of the world's deadliest diseases and South Africa ranks 9th out of the 22 countries with hardest hit of TB. Although many pieces of research have been carried out on this subject, this paper steps further by inculcating past knowledge into the model, using Bayesian approach with informative prior. Bayesian statistics approach is getting popular in data analyses. But, most applications of Bayesian inference technique are limited to situations of non-informative prior, where there is no solid external information about the distribution of the parameter of interest. The main aim of this study is to profile people living with TB in South Africa. In this paper, identical regression models are fitted for classical and Bayesian approach both with non-informative and informative prior, using South Africa General Household Survey (GHS) data for the year 2014. For the Bayesian model with informative prior, South Africa General Household Survey dataset for the year 2011 to 2013 are used to set up priors for the model 2014.
authors.affiliation: Mathematical Sciences, African Institute for Mathematical Sciences, AIMS-Cameroon, Limbe, Cameroon.
authors.forename: Oluwatobi Blessing
authors.initials: OB
authors.lastname: Ojo
authors.affiliation: School of Mathematics, Statistics and Computer Sciences, University of Kwazulu-Natal, Durban, South Africa.
authors.forename: Siaka
authors.initials: S
authors.lastname: Lougue
authors.affiliation: Mathematical Sciences, African Institute for Mathematical Sciences, AIMS-Cameroon, Limbe, Cameroon.|Department of Mathematics, Mekelle University, Mekelle, Ethiopia.
authors.forename: Woldegebriel Assefa
authors.initials: WA
authors.lastname: Woldegerima
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D008962:Models, Theoretical; D013019:South Africa / Q000453:epidemiology; D014376:Tuberculosis / Q000453:epidemiology* / Q000473:pathology