@inbook{91b10424900e43ababe37535432e81f9,
title = "Ranking Accuracy for Logistic-GEE Models",
abstract = "The logistic generalized estimating equations (logistic-gee) 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{\textquoteright} study.",
keywords = "Clustered data, Generalized Estimating Equation, Goodness-of-fit, Predictability, Ranking accuracy, OF-FIT TESTS, LONGITUDINAL DATA-ANALYSIS, CONGESTIVE-HEART-FAILURE, MANAGEMENT",
author = "Nasser Davarzani and Ralf Peeters and Evgueni Smirnov and Jo{\"e}l Karel and {Brunner-la Rocca}, Hans-peter",
year = "2016",
month = sep,
day = "21",
doi = "10.1007/978-3-319-46349-0_2",
language = "English",
isbn = "978-3-319-46348-3",
series = "Lecture Notes in Computer Science",
publisher = "Springer International Publishing AG",
pages = "14--25",
editor = "H Bostr{\"o}m and A Knobbe and C Soares and P Papapetrou",
booktitle = "IDA 2016: Advances in Intelligent Data Analysis XV",
note = "15th International Symposium on Intelligent Data Analysis (IDA) : IDA 2016 ; Conference date: 13-10-2016 Through 15-10-2016",
}