Comparison of proportional hazards and accelerated failure time models

Abstract

The field of survival analysis has experienced tremendous growth during the latter half of the 20th century. The methodological developments of survival analysis that have had the most profound impact are the Kaplan-Meier method for estimating the survival function, the log-rank test for comparing the equality of two or more survival distributions, and the Cox proportional hazards (PH) model for examining the covariate effects on the hazard function. The accelerated failure time (AFT) model was proposed but seldom used. In this thesis, we present the basic concepts, nonparametric methods (the Kaplan-Meier method and the log-rank test), semiparametric methods (the Cox PH model, and Cox model with time-dependent covariates) and parametric methods (Parametric PH model and the AFT model) for analyzing survival data.

We apply these methods to a randomized placebo-controlled trial to prevent Tuberculosis (TB) in Ugandan adults infected with Human Immunodificiency Virus (HIV). The objective of the analysis is to determine whether TB preventive therapies affect the rate of AIDS progression and survival in HIV-infected adults. Our conclusion is that TB preventive therapies appear to have no effect on AIDS progression, death and combined event of AIDS progression and death. The major goal of this paper is to support an argument for the consideration of the AFT model as an alternative to the PH model in the analysis of some survival data by means of this real dataset. We critique the PH model and assess the lack of fit. To overcome the violation of proportional hazards, we use the Cox model with time-dependent covariates, the piecewise exponential model and the accelerated failure time model. After comparison of all the models and the assessment of goodness-of-fit, we find that the log-logistic AFT model fits better for this data set. We have seen that the AFT model is a more valuable and realistic alternative to the PH model in some situations. It can provide the predicted hazard functions, predicted survival functions, median survival times and time ratios. The AFT model can easily interpret the results into the effect upon the expected median duration of illness for a patient in a clinical setting. We suggest that the PH model may not be appropriate in some situations and that the AFT model could provide a more appropriate description of the data.