Ranking Accuracy for Logistic-GEE Models

  • Nasser DavarzaniEmail author
  • Ralf Peeters
  • Evgueni Smirnov
  • Joël Karel
  • Hans-Peter Brunner-La Rocca
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9897)


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’ study.


Clustered data Generalized Estimating Equation Goodness-of-fit Predictability Ranking accuracy 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Nasser Davarzani
    • 1
    Email author
  • Ralf Peeters
    • 1
  • Evgueni Smirnov
    • 1
  • Joël Karel
    • 1
  • Hans-Peter Brunner-La Rocca
    • 2
  1. 1.Department of Data Science and Knowledge EngineeringMaastricht UniversityMaastrichtThe Netherlands
  2. 2.Department of CardiologyMaastricht University Medical CenterMaastrichtThe Netherlands

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