Bayesian logistic regression model on risk factors of type 2 diabetes mellitus

Logistic regression model has long been known and it is commonly used in analysing a binary outcome or dependent variable and connects the binary dependent variable to several independent variables. Estimates of the coefficients for the variables are obtained via the method of maximum likelihood...

Full description

Saved in:

Bibliographic Details
Main Author:	Chiaka, Emenyonu Sandra
Format:	Thesis
Language:	English
Published:	2016
Subjects:	Logistic regression analysis Diabetes - Statistical methods Bayesian statistical decision theory
Online Access:	http://psasir.upm.edu.my/id/eprint/69118/1/FS%202016%2045%20UPM%20IR.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-upm-ir.69118
record_format	uketd_dc
spelling	my-upm-ir.691182022-03-11T01:28:42Z Bayesian logistic regression model on risk factors of type 2 diabetes mellitus 2016-04 Chiaka, Emenyonu Sandra Logistic regression model has long been known and it is commonly used in analysing a binary outcome or dependent variable and connects the binary dependent variable to several independent variables. Estimates of the coefficients for the variables are obtained via the method of maximum likelihood based on the frequentist point of view. However, Bayesian analysis allows the incorporation of the prior information and the coefficients of the logistic regression model are estimated by assuming prior distribution for each of the coefficient of interest, which then combines with the likelihood function for the posterior distribution to be obtained. The Bayesian logistic regression methods made use of the metropolis hasting (Random walk algorithm) and the Gibbs sampler with the incorporation of non-informative flat prior and non-informative non-flat prior distributions to obtain the posterior distribution for each coefficient of the variables. Although we incorporated the flat prior distribution, it has been shown to be widely used in different fields of study. However, this work also incorporated a non-flat prior, which is our main research and to the best of our knowledge has not been incorporated on any T2DM dataset in Malaysia. This study evaluates the risk factors such as age, ethnicity, gender, physical activity, hypertension, body mass index, family history of diabetes and waist circumference. The coefficients of the variables mentioned above were estimated by the method of maximum likelihood and significant variables were further identified. The significant variables determined by maximum likelihood method were then estimated using the BLR method. The BLR approach via Gibbs sampler and the random walk metropolis algorithm suggests that family history of diabetes, waist circumference and the body mass index are the significant risk factors associated with the type 2 diabetes mellitus. The model results also show a slight decrease in the posterior standard deviation associated with the parameters generated from the Bayesian analysis with the non-flat prior distribution compared to the results generated from the Bayesian analysis incorporating the non-informative prior. Having seen that the difference between the models is not much, consequently from all indications, all the models are good and they exhibited model fit. Logistic regression analysis Diabetes - Statistical methods Bayesian statistical decision theory 2016-04 Thesis http://psasir.upm.edu.my/id/eprint/69118/ http://psasir.upm.edu.my/id/eprint/69118/1/FS%202016%2045%20UPM%20IR.pdf text en public masters Universiti Putra Malaysia Logistic regression analysis Diabetes - Statistical methods Bayesian statistical decision theory Adam, Mohd Bakri
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
advisor	Adam, Mohd Bakri
topic	Logistic regression analysis Diabetes - Statistical methods Bayesian statistical decision theory
spellingShingle	Logistic regression analysis Diabetes - Statistical methods Bayesian statistical decision theory Chiaka, Emenyonu Sandra Bayesian logistic regression model on risk factors of type 2 diabetes mellitus
description	Logistic regression model has long been known and it is commonly used in analysing a binary outcome or dependent variable and connects the binary dependent variable to several independent variables. Estimates of the coefficients for the variables are obtained via the method of maximum likelihood based on the frequentist point of view. However, Bayesian analysis allows the incorporation of the prior information and the coefficients of the logistic regression model are estimated by assuming prior distribution for each of the coefficient of interest, which then combines with the likelihood function for the posterior distribution to be obtained. The Bayesian logistic regression methods made use of the metropolis hasting (Random walk algorithm) and the Gibbs sampler with the incorporation of non-informative flat prior and non-informative non-flat prior distributions to obtain the posterior distribution for each coefficient of the variables. Although we incorporated the flat prior distribution, it has been shown to be widely used in different fields of study. However, this work also incorporated a non-flat prior, which is our main research and to the best of our knowledge has not been incorporated on any T2DM dataset in Malaysia. This study evaluates the risk factors such as age, ethnicity, gender, physical activity, hypertension, body mass index, family history of diabetes and waist circumference. The coefficients of the variables mentioned above were estimated by the method of maximum likelihood and significant variables were further identified. The significant variables determined by maximum likelihood method were then estimated using the BLR method. The BLR approach via Gibbs sampler and the random walk metropolis algorithm suggests that family history of diabetes, waist circumference and the body mass index are the significant risk factors associated with the type 2 diabetes mellitus. The model results also show a slight decrease in the posterior standard deviation associated with the parameters generated from the Bayesian analysis with the non-flat prior distribution compared to the results generated from the Bayesian analysis incorporating the non-informative prior. Having seen that the difference between the models is not much, consequently from all indications, all the models are good and they exhibited model fit.
format	Thesis
qualification_level	Master's degree
author	Chiaka, Emenyonu Sandra
author_facet	Chiaka, Emenyonu Sandra
author_sort	Chiaka, Emenyonu Sandra
title	Bayesian logistic regression model on risk factors of type 2 diabetes mellitus
title_short	Bayesian logistic regression model on risk factors of type 2 diabetes mellitus
title_full	Bayesian logistic regression model on risk factors of type 2 diabetes mellitus
title_fullStr	Bayesian logistic regression model on risk factors of type 2 diabetes mellitus
title_full_unstemmed	Bayesian logistic regression model on risk factors of type 2 diabetes mellitus
title_sort	bayesian logistic regression model on risk factors of type 2 diabetes mellitus
granting_institution	Universiti Putra Malaysia
publishDate	2016
url	http://psasir.upm.edu.my/id/eprint/69118/1/FS%202016%2045%20UPM%20IR.pdf
_version_	1747812666817118208

Bayesian logistic regression model on risk factors of type 2 diabetes mellitus

Similar Items