Classification of repeated measurements data using tree-based ensemble methods

In many medical applications, longitudinal data sets are available. Longitudinal data, as well as observations from paired organs, show a dependency structure which should be respected in the evaluation. Adler et al. (Comput Stat Data Anal 53(3):718–729, 2009) proposed various bootstrapping strategies for ensemble methods based on classification trees for two measurements of paired organs. These strategies have shown to improve the classification performance compared to the traditional approach, where only one observation per subject is used. We extend the methodology to the situation, where an arbitrary number of observations per individual are available and investigate the performance of the proposed methods with bagged classification trees (bagging) and random forests in the situation of longitudinal data. Moreover, we adapt the estimation of classification performance criteria to repeated measurements data. The clinical data set consists of morphological examinations of both eyes of glaucoma patients and healthy controls over a time period of up to 7 years. The performance of our modified classifiers is evaluated by a subject-based leave-one-out bootstrap ROC analysis. Simulation results and results for the glaucoma data set demonstrate that our proposal is an improvement of adhoc strategies and of the use all measurements of each subject or block strategy.