Performance Evaluation of Support Vector Regression Models for Survival Analysis: A Simulation Study

Desirable features of support vector regression (SVR) models have led to researchers extending them to survival problems. In current paper we evaluate and compare performance of different SVR models and the Cox model using simulated and real data sets with different characteristics. Several SVR models are applied: 1) SVR with only regression constraints (standard SVR); 2) SVR with regression and ranking constraints; 3) SVR with positivity constraints; and 4) L1-SVR. Also, a SVR model based on mean residual life is proposed. Our findings from evaluation of real data sets indicate that for data sets with high censoring rate and high number of features, SVR model significantly outperforms the Cox model. Simulated data sets also show similar results. For some real data sets L1-SVR has a significantly degraded performance in comparison to the standard SVR. Performance of other SVR models is not substantially different from the standard SVR with the real data sets. Nevertheless, the results of simulated data sets show that standard SVR slightly outperforms SVR with regression and ranking constraints. Keywords—support vector machines; support vector regression; survival analysis; simulation study; Cox model; mean residual life