Volume 5, Issue 4, August 2017, Page: 127-133
Prediction of Survival of HIV/AIDS Patients from Various Sources of Data Using AFT Models
Markos Abiso Erango, School of Mathematical and Statistical Sciences, Hawassa University, Awasa, Ethiopia
Ayele Taye Goshu, School of Mathematical and Statistical Sciences, Hawassa University, Awasa, Ethiopia
Received: Apr. 28, 2017;       Accepted: May 16, 2017;       Published: Jul. 7, 2017
DOI: 10.11648/j.sjams.20170504.11      View  1437      Downloads  75
The aim of this paper is to predict and compare the survival of HIV/AIDS patients under ART follow-up in three different hospitals in Ethiopia. Three data sets with total 1304 patients were considered. Three parametric accelerated failure time distributions: lognormal, loglogistic and Weibull are used to analyze, predict and compare survival probabilities of the patients. The results indicate that the empirical hazard rates of the three data sets reveal maximal peaks. The patients from Arba Minch hospital seems to have highest event intensity. The AFT loglogistic model is selected to best fit to each of the data sets. Different covariates except TB infection status are found to affect patients' survival at each of the hospitals. Patients with TB infection at baseline tend to have shorter survival time as compare to one with no TB infection, with significant differences of survive time between the two groups. Patients under follow-up at Shashemene hospital tend have consistently highest survival probabilities in both TB positive and negative groups. Patients from Bale Robe hospital tend to have longest survival time, while those from Arba Minch hospital have shortest survival time. Patients with bedridden status have the shortest survival time. The AFT-loglogistic is recommended in modelling time-to-event data considered in this study. The results are unique to each hospital implying that patients' care and intervention needs to be specific.
Accelerated Failure Time, HIV/AIDS, Prediction, Survival Analysis
To cite this article
Markos Abiso Erango, Ayele Taye Goshu, Prediction of Survival of HIV/AIDS Patients from Various Sources of Data Using AFT Models, Science Journal of Applied Mathematics and Statistics. Vol. 5, No. 4, 2017, pp. 127-133. doi: 10.11648/j.sjams.20170504.11
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Andinet W., San, M. (2010). Determinantsof Survival in Adult HIV Patients on Antiretroviral Therapy in Oromia, Ethiopia. Global Health Action, 3:5398.
Azin, S. A. (2010). An Overview on the 2008 UNAIDS Report on the Global AIDS Epidemic. Iranian Journal of Epidemiology, 6(2), 56–58.
Yu, M., Taylor, J., and Sandler, H. (2008). Individualized Prediction in Prostate Cancer Studies using a Joint Longitudinal-Survival-Cure Model. Journal of the American Statistical Association, 103, 178-187.
Markos A. Erango, Ayele T. Goshu, Gemeda B. Buta and Ahmed H. Dessiso(2017). Bayesian Joint Modeling of Survival of HIV/AIDS Patients using Accelerated Failure TimeDataand Longitudinal CD4 Cell Counts. British Journal of Medicine & Medical Research, 20(6), 1-12. DOI: 10.9734/BJMMR/2017/32123.
Hosmer, D. W. and Lemeshow, S. (1999). Applied Survival Analysis. John Wiley and Sons, Inc., New York.
Collett, D. (2003). Modelling Survival Data in Medical Research. Chapman and Hall, London.
Lee, E. T., and Wang, J. W. (2003). Statistical Methods for Survival Data Analysis. Third Edition. Wiley, New York.
Hayat, E. A, Suner, A., Uyar, B., Dursun, Ö., Orman, M. N., Kitapçioğlu, G. (2010). Comparison of Five Survival Models: Breast Cancer Registry Data from Ege University Cancer Research Center. TurkiyeKlinikleri J Med Sci, 30(5).
Klein, J. P. and Moeschberger, M. L. (2003). Survival Analysis Techniques for Censored and Truncated Data. Second Edition. Statistics for Biology and Health, Springer LLC.
Burnham, Kenneth P. and David R. Anderson. 2002. Model Selection and Multi model Inference: A Practical Information-Theoretical Approach. 2nd ed. New York: Springer.
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