 Joint Modeling of Longitudinal CD4 Count and Time-to-Death of HIV/TB Co-infected Patients: A Case of Jimma University Specialized HospitalAboma Temesgen (),
Abdisa Gurmesa and
Yehenew Getchew
Annals of Data Science, 2018, vol. 5, issue 4, No 9, 659-678 Abstract: Abstract Tuberculosis (TB) and HIV have been closely linked since the emergence of AIDS; TB enhances HIV replication by accelerating the natural evolution of HIV infection which is the leading cause of sickness and death of peoples living with HIV/AIDS. To improve their life the co-infected patients are started to take antiretroviral treatment as patient started to take ART it is common to measure CD4 and other clinical outcomes which is correlated with survival time. However, the separate analysis of such data does not handle the association between the longitudinal measured out come and time-to-event where the joint modeling does to obtain valid and efficient survival time. Joint modeling of longitudinally measured CD4 and time-to death to understand their association. Furthermore, the study identifies factors affecting the mean change in square root CD4 measurement over time and risk factors for the survival time of HIV/TB co-infected patients. The study consists of 254 HIV/TB co-infected patients who were 18 years old or older and who were on antiretroviral treatment follow up from first February 2009 to fist July 2014 in Jimma University Specialized Hospital, West Ethiopia. First, data were analyzed using linear mixed model and survival models separately. After having appropriate separate models using Akaki information criteria, different joint models employed with different random effects longitudinal model and different shared parameters association structure of survival model and compared with deviance information criteria score. The linear mixed model showed functional status, weight, linear time and quadratic time effects have significant effect on the mean change of CD4 measurement over time. The Cox and Weibull survival model showed base line weight, baseline smoking, separated marital status group and base line functional status have significant effect on hazard function of the survival time whereas the joint model showed subject specific base line value; subject specific linear and quadratic slopes of CD4 measurement of were significantly associated with the survival time of co-infected patient at 5% significance levels. The longitudinally measured CD4 count measurement marker process is significantly associated with time to death and subject specific quadratic slope growth of CD4 measurement, base line clinical stage IV and smoking is the high risk factors that lower the survival time of HIV/TB co-infected patients. Since the longitudinally measured CD4 measurement is correlated with survival time joint modeling are used to handle the associations between these two processes to obtain valid and efficient survival time. Keywords: Survival analysis; Longitudinal analysis; Cox PH; Linear mixed model; Joint modeling; HIV–TB (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:aodasc:v:5:y:2018:i:4:d:10.1007_s40745-018-0157-0 Ordering information: This journal article can be ordered from DOI: 10.1007/s40745-018-0157-0 Access Statistics for this article Annals of Data Science is currently edited by Yong Shi More articles in Annals of Data Science from Springer |
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