A Joint Modeling Of Longitudinal And Survival Data With Application To Hiv-infected Patients Under Haart Follow-up A Case Of Mekelle General Hospital Ethiopia

Despite tremendous progress in the control of the global HIV epidemic, the burden of HIV isrnstill severe in Sub-Saharan Africa. Longitudinal and survival data frequently observed togetherrnin practice and useful for analysis of HIV related data. The separate analyses of longitudinalrnand survival endpoints may not be adequate and could lead to ine cient estimation or biasedrnresults. Joint modeling approaches correct for this bias by accounting for the association betweenrnthe two responses. The main purpose of this study was to jointly model and analyze longitu-rndinal and survival endpoints with application to retrospective cohort data of 469 HIV-infectedrnpatients under HAART follow-up in Mekelle General Hospital, Tigray, Ethiopia. The analysisrnconsists of exploratory data analysis and tting three di erent models namely; a linear mixedrne ects model for the longitudinal data, a semi-parametric survival model for the time-to-eventrndata and a joint modeling of the two responses via shared random-e ects approach. The resultsrnof both the separate and joint analyses are consistent. However, the use of a joint analysisrncompared to independent models shows a reduction in the standard errors which indicates thatrnmore adequate and e cient inferences can be made by using joint model estimates. The esti-rnmated association parameter ( ) in the joint model is -0.138 (with 95% CI: -0.196 À€€ -0.079)rnand statistically signi cant (p ð€€€ value < 0:0001). This indicates that there is strong evidencernof association between the e ect of the longitudinal biomarker to the risk of death. The resultsrnindicates that higher initial values of CD4 cells is associated with a better survival. Further-rnmore, patients with lower initial weight, being male, late WHO clinical stage, being ambulatoryrnand bedridden were associated with higher risk of death. Future extension of this research couldrnpossibly be to account for missing data and attempt should be given to health workers and datarnclerks working with patients under HAART to improve the quality of the data records of patients.rnKeywords: HAART, HIV/AIDS Data, Joint Modeling, Longitudinal Data Analysis, SurvivalrnData Analysis

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