PREDICTING GLAUCOMA PROGRESSION USING DECISION TREES FOR CLUSTERED DATA BY GOODNESS OF SPLIT

Abstract

Glaucoma is a chronic, progressive and potentially blinding condition. Predicting which patients will experience significant progression is recognized as a crucially needed development in the management of this disease. Application of the CART (Classification And Regression Trees) methodology has demonstrated that certain patterns of visual field findings may convey greater predictive information for glaucoma progression. However, the current standard classification tree method was developed for uncorrelated data. In this article a classification tree method is extended to correlated binary data. The robust Wald test statistic from generalized estimating equations (GEE) is used to measure the between-node difference while adjusting for correlation between the eyes of a patient. The proposed method is assessed through simulations conducted under a variety of model configurations and is used to analyze the perimetry and psychophysics in glaucoma (PPIG) study data. Employing an amalgamation algorithm from the result of a best-sized tree, each eye is classified to one of two prognosis categories (less likely, or more likely, to progress). Receiver operating characteristics (ROC) and area under the curve (AUC) indicate that the proposed method, applied to data from both eyes of the same patient, provides much improved prediction accuracy compared with application of standard CART method to the same PPIG data.