DETERMINING THE FACTORS THAT AFFECT LONGITUDINAL  INTRAOCULAR PRESSURE CHANGE AND TIME TO BLINDNESS OF  GLAUCOMA PATIENTS: BAYESIAN JOINT MODELING APPROACH  AT FELEGE HIWOT TEACHING AND SPECIALIZED HOSPITAL,  BAHIR DAR, ETHIOPIA

MINILIK DERSEH

dc.contributor.author	MINILIK DERSEH
dc.date.accessioned	2021-05-17T05:49:57Z
dc.date.available	2021-05-17T05:49:57Z
dc.date.issued	2021-05-21
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/12095
dc.description.abstract	Background: Glaucoma is a neurodegenerative condition that affects the eye and is associated with increased intraocular pressure (IOP). IOP is the fluid pressure of the eye. IOP is carefully regulated and disturbance often implicated in the development of pathologies such as glaucoma, uveitis and retinal detachment. The aim of the present study was identifying factors that have strong association with the longitudinal IOP and the survival experience (time to blindness) of glaucoma patients attending ophthalmology clinic at FHSCH, Bahir Dar, Ethiopia using Bayesian joint model analysis. Methods: A longitudinal and time-to-event study with data obtained from FHSCH, glaucoma patients enrolled in ophthalmology clinic, the measurement of IOP change approximately in every six months and the time of an event occurring were taken. The study subjects were enrolled between the period 1st January 2016 and 1st January 2020. A total of 328 patients, who fulfill the inclusion criteria were selected for the study. Data were explored using basic descriptive statistics and individual and mean profile plots over a period of study time. The censoring status of categorical covariates was also presented. Bayesian linear mixed model for the longitudinal data and Kaplan-Meier, Bayesian weibull proportional hazard model for the survival data analysis were used along with their model comparison, model estimation, model diagnosis and missing data analysis. Results: The analysis included 328 individuals with 9 for maximum and 2 for minimum repeated measurement of IOP change, including the baseline. From the Bayesian linear mixed model variables like observation time, age, place of residence, gender, cup-disk ratio of patients, type of medicine (like Pilocarpin, Timolol with Pilocarpin, Timolol with Diamox with Pilocarpin) and blood pressure of the glaucoma patients were have an association with the IOP change over time. But type of medicine (Diamox and Timolol with Diamox) were not affect the IOP change over time. The Bayesian weibull PH model covariates age, blood pressure, diabetic, Pilocarpin, Timolol with Pilocarpin, Timolol with Diamox, Timolol with Diamox with Pilocarpin, medium treatment duration, long treatment duration and advanced stage of glaucoma of patients significantly determines the hazard function. Bayesian weibull PH model is selected the appropriate parametric model. Bayesian joint analysis answers question that cannot be possible by Bayesian separate models	en_US
dc.language.iso	en	en_US
dc.subject	Statistics	en_US
dc.title	DETERMINING THE FACTORS THAT AFFECT LONGITUDINAL INTRAOCULAR PRESSURE CHANGE AND TIME TO BLINDNESS OF GLAUCOMA PATIENTS: BAYESIAN JOINT MODELING APPROACH AT FELEGE HIWOT TEACHING AND SPECIALIZED HOSPITAL, BAHIR DAR, ETHIOPIA	en_US
dc.type	Thesis	en_US