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.
| Published in | Science Journal of Applied Mathematics and Statistics (Volume 5, Issue 4) |
| DOI | 10.11648/j.sjams.20170504.11 |
| Page(s) | 127-133 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Accelerated Failure Time, HIV/AIDS, Prediction, Survival Analysis
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APA Style
Markos Abiso Erango, Ayele Taye Goshu. (2017). Prediction of Survival of HIV/AIDS Patients from Various Sources of Data Using AFT Models. Science Journal of Applied Mathematics and Statistics, 5(4), 127-133. https://doi.org/10.11648/j.sjams.20170504.11
ACS Style
Markos Abiso Erango; Ayele Taye Goshu. Prediction of Survival of HIV/AIDS Patients from Various Sources of Data Using AFT Models. Sci. J. Appl. Math. Stat. 2017, 5(4), 127-133. doi: 10.11648/j.sjams.20170504.11
AMA Style
Markos Abiso Erango, Ayele Taye Goshu. Prediction of Survival of HIV/AIDS Patients from Various Sources of Data Using AFT Models. Sci J Appl Math Stat. 2017;5(4):127-133. doi: 10.11648/j.sjams.20170504.11
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Download@article{10.11648/j.sjams.20170504.11,
author = {Markos Abiso Erango and Ayele Taye Goshu},
title = {Prediction of Survival of HIV/AIDS Patients from Various Sources of Data Using AFT Models},
journal = {Science Journal of Applied Mathematics and Statistics},
volume = {5},
number = {4},
pages = {127-133},
doi = {10.11648/j.sjams.20170504.11},
url = {https://doi.org/10.11648/j.sjams.20170504.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20170504.11},
abstract = {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.},
year = {2017}
}
TY - JOUR T1 - Prediction of Survival of HIV/AIDS Patients from Various Sources of Data Using AFT Models AU - Markos Abiso Erango AU - Ayele Taye Goshu Y1 - 2017/07/07 PY - 2017 N1 - https://doi.org/10.11648/j.sjams.20170504.11 DO - 10.11648/j.sjams.20170504.11 T2 - Science Journal of Applied Mathematics and Statistics JF - Science Journal of Applied Mathematics and Statistics JO - Science Journal of Applied Mathematics and Statistics SP - 127 EP - 133 PB - Science Publishing Group SN - 2376-9513 UR - https://doi.org/10.11648/j.sjams.20170504.11 AB - 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. VL - 5 IS - 4 ER -
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