Model averaging prediction for survival data with time-dependent effects

It is a fundamental task to predict patients’ survival outcomes in clinical research. As an extension of the Cox proportional hazards model, the time-dependent coefficient Cox model is typically utilized for time-to-event data with time-dependent effects. When the number of covariates is large, the curse of dimensionality emerges for most existing methods. To overcome the limitation and improve predictive performance, a semiparametric model averaging approach is proposed for the time-dependent coefficient Cox model. We introduce a novel criterion to estimate model weights and demonstrate its theoretical properties. Extensive simulation studies are conducted to compare the proposed technique with existing competitive methods. A real clinical data set is also analyzed to illustrate the advantages of our approach.