Tree-structured prognostic classification for censored survival data: Validation of computationally inexpensive model selection criteria

The performance of computationally inexpensive model selection criteria in the context of tree structured prediction is discussed. It is shown through a simulation study that no one model selection criterion exhibits a uniformly superior performance over a wide range of scenarios. Therefore, a two-stage approach for model selection is suggested and shown to perform satisfactorily. A computationally efficient method of tree-growing within the RECursive Partition and AMalgamation (RECPAM) framework is suggested. The computationally efficient algorithm gives identical results as the original RECPAM tree-growing algorithm. An example of medical data analysis for developing prognostic classification is presented.