Empirical Study on High-Dimensional Variable Selection and Prediction Under Competing Risks

Competing risk analysis considers event times due to multiple causes, or of more than one event types. Commonly used regression models for such data include (1) cause-specific hazards model, which focuses on modeling one type of event while acknowledging other event types simultaneously; and (2) subdistribution hazards model, which links the covariate effects directly to the cumulative incidence function. Their use and in particular statistical properties in the presence of high-dimensional predictors are largely unexplored. We study the accuracy of prediction and variable selection of existing statistical learning methods under both models using extensive simulation experiments, including different approaches to choosing penalty parameters in each method.