Demonstrating the Performance of Accelerated Failure Time Model Over Cox-PH Model of Survival Data Analysis with Application to HIV-Infected Patients Under HAART
American Journal of Theoretical and Applied Statistics
Volume 8, Issue 6, November 2019, Pages: 193-202
Received: May 3, 2019;
Accepted: Oct. 24, 2019;
Published: Oct. 31, 2019
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Getnet Bogale Begashaw, Department of Statistics, College of Natural Science, Wollo University, Dessie, Ethiopia
Human Immunodeficiency Virus (HIV) is a virus that kills CD4 cells. These CD4 cells are white blood cells that fight infection. CD4 count is like a snapshot of how well our immune system is functioning. Studying the way of CD4+ count over time provides an insight to the disease evolution. This study was considering the data of HIV/AIDS patients who were undergoing Antiretroviral Therapy in the ART clinic of Menellik II Referral Hospital, Addis Ababa, Ethiopia, during the period 1st January 2014 to 31st December 2017. For separate survival model log-logistic model is more appropriate for the survival data than other parametric models. Therefore; functional status and regimen class are significant covariates in determining the hazard function patients. Log rank and Wilcoxon tests showed that the significant difference in survival situation among the categorical variables selected for this study sex, marital status, functional status, WHO-clinical stages and regimen class subgroups. But, there was no significant difference in the time-to-event between subgroups of sex, Marital Status and WHO clinical Stage, while, Regimen Class and Functional Status there was a significant difference in the time-to-event between subgroups.
Getnet Bogale Begashaw,
Demonstrating the Performance of Accelerated Failure Time Model Over Cox-PH Model of Survival Data Analysis with Application to HIV-Infected Patients Under HAART, American Journal of Theoretical and Applied Statistics.
Vol. 8, No. 6,
2019, pp. 193-202.
Asfaw A, et al. (2015) CD4 cell count trends after commencement of antiretroviral therapy among HIV-infected patients in Tigray, Northern Ethiopia: a retrospective cross-sectional study. PLoS ONE.10 (3): e0122583.
Awoke T, Worku A, Kebede Y, Kasim A, Birlie B, Braekers R, et al. (2016) Modeling Outcomes of First-Line Antiretroviral Therapy and Rate of CD4 Counts Change among a Cohort of HIV/AIDS Patients in Ethiopia: A Retrospective Cohort Study. PLoS ONE 11 (12): e0168323. doi: 10.1371/journal.pone.0168323.
Ayesha B. M. Kharsany and Quarraisha A. Karim; 2016, 10, 34-48, HIV Infection and AIDS in Sub-Saharan Africa: Current Status, Challenges and Opportunities, DOI: 10.2174/1874613601610010034, The Open AIDS Journal.
C. Brombin, (2016). Evaluating treatment effect within a multivariate stochastic ordering framework: Nonparametric combination methodolog y applied to a study on multiple sclerosis. Statistical Methods in Medical Research, 2016, Vo l. 25 (1) 366384, DOI: 10.1177/0962280212454203.
Collett, D. (2004), Modeling Survival Data in Medical Research, 2nd edition, Chapman and Hall. Cox DR. Regression models and life tables. Journal of the Royal Statistical Society Series BStatistical Methodology. 1972; 34: 187-220.
Cox, D. R. (1972). Regression models and Life Tables (with Discussion). Journal of the Royal Statistical Society. Series B, 34, 187-220.
Dinberu S., et al., (2017). Risk Factors for Mortality among Adult HIV/AIDS Patients Following Antiretroviral Therapy in Southwestern Ethiopia: An Assessment through Survival Models, International Journal of Environmental Research and Public Health, vol (14), 296; doi: 10. 3390/ijerph14030296.
East S, Africa S-S. (2010). Towards universal access: scaling up priority HIV/AIDS interventions in the health sector. Europe. 85: 000.
Feleke DG, Yemanebrhane N, Gebretsadik D (2017). Nutritional Status and CD4 Cell Counts in HIV/AIDS Patients under Highly Active Antiretroviral Therapy in Addis Ababa, Ethiopia. J AIDS Clin Res 8: 688. doi: 10.4172/2155-6113.1000688.
Florence E, et al. (2003). Factors associated with a reduced CD4 lymphocyte count response to HAART despite full viral suppression in the EuroSIDA study. HIV Med. 4 (3): 255-62.
Hosmer DW JR, Lemenshow S, MAY S., (2008), Applied survival analysis: regression modeling of time-to-event data, second edition. Hoboken: WileyInterscience.
Hu D. J., Dondero T. J., Rayfield M. A., George R., Schochetman G., (2016). The emerging genetic diversity of HIV, the importance of global surveillance for diagnostic research and prevention. JAMA, 275 (3), 210-216.
Kaufmann GR, et al. (2003). CD4 T-lymphocyte recovery in individuals with advanced HIV-1 infection receiving potent antiretroviral therapy for 4 years: the Swiss HIV Cohort Study. Arch Intern Med. 163 (18): 218795.
Kebadu T., (2016). Modeling CD4 + Cell Counts of HIV-Positive Patients Following Antiretroviral Therapy (ART): A Case of Yekatit 12 Hospital, Addis Ababa.
Santos ACOD, Almeida AMR (2013) Nutritional status and CD4 cell counts in patients with HIV/AIDS receiving antiretroviral therapy. Revista da Sociedade Brasileira de Medicina Tropical 46: 698-703.
Smith CJ, et al. (2004). Factors inflencing increases in CD4 cell counts of HIV positive persons receiving long-term highly active antiretroviral therapy. J Infect Dis. 190 (10): 1860-8.
UNAIDS. Fact sheet 2014: Global statistics.
UNAIDS: Fact Sheet (2016). In. Edited by Communications and Global Advocacy. Geneva, Switzerland.
van Leth F, et al. (2004). Comparison of fist-line antiretroviral therapy with regimens including nevirapine, efavirenz, or both drugs, plus stavudine and lamivudine: a randomised open-label trial, the 2NN Study. Lancet. 363 (9417): 1253-63.
WHO, (2007). Laboratory Guidelines for enumerating CD4 T Lymphocytes in the context of HIV/AIDS.