Parametric modelling of survival following HIV and AIDS in the era of highly active antiretroviral therapy: data from Australia

Abstract

Parametric survival models have not previously been applied to survival following a diagnosis of HIV/ AIDS in Australia. Four different parametric models-exponential, Weibull, log-normal and log-logistic-were applied to data both on HIV-positive cases and on cases diagnosed with AIDS collected through the national HIV/AIDS surveillance system. Using likelihood based goodness-of-fit criteria the Weibull model was found to be the best-fitted model for predicting survival following a diagnosis of HIV infection without and with a diagnosis of AIDS. Several covariates-age, sex, combined HIV exposure category, CD4 cell counts, antiretroviral treatment and AIDS-defining illnesses-were included in the parametric model to predict factors associated with future mortality. Predicted deaths were in agreement with the observed deaths following HIV infection and AIDS. The Weibull model will be applied for future projections of deaths from HIV/AIDS.

Risk Factors for Mortality among Adult HIV/AIDS Patients Following Antiretroviral Therapy in Southwestern Ethiopia: An Assessment through Survival Models

“…In our study, the Weibull survival model exhibited the smallest AIC, indicating its ability to fit the data. Previous longitudinal studies in Australia and England have also recognized the Weibull regression model as the best model for fitting the time until HIV/AIDS-related death data [3,32]. Our findings also agree with a study that compared parametric models for breast cancer survival data in India [14].…”

“…Different clinical, demographic, socio-economic, and behavioural factors have been reported to be related to the survival of HIV-infected patients under ART [3,4]. In Sub-Saharan Africa, studies have reported an important correlation between mortality in HIV-patients under treatment and late diagnosis of HIV, the delay in ART initiation, an advanced World Health Organization (WHO) clinical stage, low CD4 counts, high viral loads, a low body weight, low haemoglobin levels, and poor socio-economic conditions [5,6,7,8].…”

“…Both semi-parametric and parametric survival models have been used to predict the effects of different potentially influential factors on the time until HIV/AIDS-related death (survival time) [3,9]. Although the Cox regression model has been widely used for this purpose due to its minimal requirements of assumptions for predicting the prognostic factors associated with survival [3,13], parametric models have been demonstrated to be more accurate in creating projections regarding the risk of mortality beyond the observed follow-up period [14,15].…”

“…Although the Cox regression model has been widely used for this purpose due to its minimal requirements of assumptions for predicting the prognostic factors associated with survival [3,13], parametric models have been demonstrated to be more accurate in creating projections regarding the risk of mortality beyond the observed follow-up period [14,15]. In this paper, we report the results obtained using three of the most popular parametric survival models (i.e., exponential, Weibull, and log-logistic) and one semi-parametric (Cox proportional hazard model) model; these models analyzed non-concurrent retrospective cohort data from patients with HIV/AIDS, who initiated ART treatment between January 2006 and December 2010 at the Jimma University Specialized Hospital (JUSH) in Ethiopia, in order to identify the predictive factors of HIV/AIDS-related mortality.…”

Determination of Predictors Associated With HIV/AIDS Patients on ART Using Accelerated Failure Time Model for Interval Censored Survival Data

“…The result of our study has a strong inclination for the Lognormal AFT model as the most suitable one; better than other based on AIC and BIC values. In an earlier paper by Nakhaee and Law (2011) suggested that Weibull is the best fitted parametric model for predicting survival following a diagnosis of HIV infection without and with a diagnosis of AIDS and could be used for future projections of deaths from HIV/AIDS. Their analysis and inference was based on right censored data.…”