Demonstrating the Performance of Accelerated Failure Time Model over Cox-PH Model of Survival Data Analysis with Application to HIV-Infected Patients under HAART
Abstract Background: 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. Methods: 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. The data was analyzed in separate survival models i.e non parametric, semi parametric (Cox PH) and parametric survival model (AFT models). For the purpose of model diagnosis cox-snail residual analysis were incorporated. Results: 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. . In the Log-logistic model, among the covariates we have included in the survival model: functional status (working subgroup) and regimen class (all subgroup) were significant at 5% level of significance. But, sex, age, baseCD4, marital status and WHO-clinical stage are not significance at 5% significance level. Using cox-snail residual shows proportionality not satisfied for these WHO stage, regimen class and marital status. Conclusions: 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.