A comparison of Cox proportional hazards and artificial neural network models for medical prognosis

Modeling survival of populations and establishing prognoses for individual patients are important activities in the practice of medicine. For patients with diseases that may extend for several years, in particular, accurate assessment of survival probabilities is essential. New methods, such as neural networks, have been used increasingly to model disease progression. Their advantages and disadvantages, when compared to statistical methods such as Cox proportional hazards, have seldom been explored in real-world data. In this study, we compare the performances of a Cox model and a neural network model that are used as prognostic tools for a set of people living with AIDS. We modeled disease progressions for patients who had AIDS (according to the 1993 CDC definition) in a set of 588 patients in California, using data from the ATHOS project. We divided the study population into 10 training and 10 test sets and evaluated the prognostic accuracy of a Cox proportional hazards model and of a neural network model by determining sensitivities, specificities, positive and negative predictive values for an arbitrary threshold (0.5), and the areas under the receiver operating characteristics (ROC) curves that utilized all possible thresholds for intervals of 1 yr following the diagnosis of AIDS. There was no evidence that the Cox model performed better than did the neural network model or vice versa, but the former method had the advantage of providing some insight on which variables were most influential for prognosis. Nevertheless, it is likely that the assumptions required by the Cox model may not be satisfied in all data sets, justifying the use of neural networks in certain cases.