A nonparametric method for classification trees using grouped covariates

A group of variables are commonly seen in diagnostic medicine when multiple prognostic factors are aggregated into a composite score to represent the risk profile. A model selection method considers these covariates as all-in or all-out types. Model selection procedures for grouped covariates and their applications have thrived in recent years, in part because of the development of genetic research in which gene-gene or gene-environment interactions and regulatory network pathways are considered groups of individual variables. However, little has been discussed on how to utilize grouped covariates to grow a classification tree. In this paper, we propose a nonparametric method to address the selection of split variables for grouped covariates and their following selection of split points. Comprehensive simulations were implemented to show the superiority of our procedures compared to a commonly used recursive partition algorithm. The practical use of our method is demonstrated through a real data analysis that uses a group of prognostic factors to classify the successful mobilization of peripheral blood stem cells.