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
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Date
2017-06
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Addis Abeba university
Abstract
Despite tremendous progress in the control of the global HIV epidemic, the burden of HIV is
still severe in Sub-Saharan Africa. Longitudinal and survival data frequently observed together
in practice and useful for analysis of HIV related data. The separate analyses of longitudinal
and survival endpoints may not be adequate and could lead to ine cient estimation or biased
results. Joint modeling approaches correct for this bias by accounting for the association between
the two responses. The main purpose of this study was to jointly model and analyze longitu-
dinal and survival endpoints with application to retrospective cohort data of 469 HIV-infected
patients under HAART follow-up in Mekelle General Hospital, Tigray, Ethiopia. The analysis
consists of exploratory data analysis and tting three di erent models namely; a linear mixed
e ects model for the longitudinal data, a semi-parametric survival model for the time-to-event
data and a joint modeling of the two responses via shared random-e ects approach. The results
of both the separate and joint analyses are consistent. However, the use of a joint analysis
compared to independent models shows a reduction in the standard errors which indicates that
more adequate and e cient inferences can be made by using joint model estimates. The esti-
mated association parameter ( ) in the joint model is -0.138 (with 95% CI: -0.196 -0.079)
and statistically signi cant (p value < 0:0001). This indicates that there is strong evidence
of association between the e ect of the longitudinal biomarker to the risk of death. The results
indicates that higher initial values of CD4 cells is associated with a better survival. Further-
more, patients with lower initial weight, being male, late WHO clinical stage, being ambulatory
and bedridden were associated with higher risk of death. Future extension of this research could
possibly be to account for missing data and attempt should be given to health workers and data
clerks working with patients under HAART to improve the quality of the data records of patients.
Keywords: HAART, HIV/AIDS Data, Joint Modeling, Longitudinal Data Analysis, Survival
Data Analysis
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Keywords
HAArt, HIV/Aids Data, Joint Modeling, Longitudinal Data Analysis, Survival Data Analysis