Accelerated Failure Time Model with Weighted Least-Squares Estimation: Application on Survival of HIV Positives

Abstract BackgroundSurvival analysis is the most appropriate method of analysis for time to event data. The classical accelerated failure time model is a more powerful and interpretable model than the Cox proportional hazards model provided that, model imposed distributional and homoscedasticity assumptions satisfied. However, most of the real data are heteroscedastic which violate the fundamental assumption and consequently, the statistical inference could be erroneous in accelerated failure time modeling. Weighted least squares estimation for accelerated failure time model is an efficient semi-parametric approach for time to event data without the homoscedasticity assumption, which is developed recently and not often utilized for real data analysis. Thus, the study was conducted to ascertain the predictive performance of weighted least squares estimation method over the classical methods.MethodsWe analyzed a sample of 203 real Antiretroviral Therapy dataset. We compared the results from clasical methods of estimation for accelerated failure time model with the results revealed from the weighted least squares estimation.ResultsWe found that the data are heteroscedastic. The weighted least squares estimation revealed more accurate, and efficient estimates of covariates effect. It also detected more significant covariates. Accordingly, survival of HIV positives varies with age, weight, functional status, CD4 percent, and clinical stages.ConclusionsThe weighted least squares estimation performed best in predicting the survival of HIV patients. Thus, we recommend future researchers should utilize weighted least squares estimation rather than the classical methods when the homoscedasticity assumption is violated.