Ranking Accuracy for Logistic-GEE Models

The logistic Generalized Estimating Equations (logisticGEE) models have been extensively used for analyzing clustered binary data. However, assessing the goodness-of-fit and predictability of these models is problematic due to the fact that no likelihood is available and the observations can be correlated within a cluster. In this paper we propose a new measure for estimating the generalization performance of the logistic GEE models, namely ranking accuracy for models based on clustered data (RAMCD). We define RAMCD as the probability that a randomly selected positive observation is ranked higher than randomly selected negative observation from another cluster. We propose a computationally efficient algorithm for RAMCD. The algorithm can be applied for two cases: (1) when we estimate RAMCD as a goodness-of-fit criterion and (2) when we estimate RAMCD as a predictability criterion. This is experimentally shown on clustered data from a simulation study and a biomarkers' study.