, Volume 77, Issue 1, pp 106–126

Likelihood-Based Clustering of Meta-Analytic SROC Curves


  • Heinz Holling
    • University of Münster
  • Walailuck Böhning
    • University of Münster
    • School of Mathematics and Southampton Statistical Sciences Research InstituteUniversity of Southampton

DOI: 10.1007/s11336-011-9236-2

Cite this article as:
Holling, H., Böhning, W. & Böhning, D. Psychometrika (2012) 77: 106. doi:10.1007/s11336-011-9236-2


Meta-analysis of diagnostic studies experience the common problem that different studies might not be comparable since they have been using a different cut-off value for the continuous or ordered categorical diagnostic test value defining different regions for which the diagnostic test is defined to be positive. Hence specificities and sensitivities arising from different studies might vary just because the underlying cut-off value had been different. To cope with the cut-off value problem interest is usually directed towards the receiver operating characteristic (ROC) curve which consists of pairs of sensitivities and false-positive rates (1-specificity). In the context of meta-analysis one pair represents one study and the associated diagram is called an SROC curve where the S stands for “summary”. In meta-analysis of diagnostic studies emphasis has traditionally been placed on modelling this SROC curve with the intention of providing a summary measure of the diagnostic accuracy by means of an estimate of the summary ROC curve. Here, we focus instead on finding sub-groups or components in the data representing different diagnostic accuracies. The paper will consider modelling SROC curves with the Lehmann family which is characterised by one parameter only. Each single study can be represented by a specific value of that parameter. Hence we focus on the distribution of these parameter estimates and suggest modelling a potential heterogeneous or cluster structure by a mixture of specifically parameterised normal densities. We point out that this mixture is completely nonparametric and the associated mixture likelihood is well-defined and globally bounded. We use the theory and algorithms of nonparametric mixture likelihood estimation to identify a potential cluster structure in the diagnostic accuracies of the collection of studies to be analysed. Several meta-analytic applications on diagnostic studies, including AUDIT and AUDIT-C for detection of unhealthy alcohol use, the mini-mental state examination for cognitive disorders, as well as diagnostic accuracy inspection data on metal fatigue of aircraft spare parts, are discussed to illustrate the methodology.

Key words

C.A.MAN diagnostic testing meta-analysis sensitivity specificity summary receiver operating characteristic (SROC) summary statistics approach

Copyright information

© The Psychometric Society 2011