Covariate adjusted classification trees.

In studies that compare several diagnostic groups, subjects can be measured on certain features and classification trees can be used to identify which of them best characterize the differences among groups. However, subjects may also be measured on additional covariates whose ability to characterize group differences is not meaningful or of interest, but may still have an impact on the examined features. Therefore, it is important to adjust for the effects of covariates on these features. We present a new semi-parametric approach to adjust for covariate effects when constructing classification trees based on the features of interest that is readily implementable. An application is given for postmortem brain tissue data to compare the neurobiological characteristics of subjects with schizophrenia to those of normal controls. We also evaluate the performance of our approach using a simulation study.