Journal of Clinical Epidemiology
Volume 62, Issue 12 , Pages 1284-1291, December 2009

Associating explanatory variables with summary receiver operating characteristic curves in diagnostic meta-analysis

  • Taye Hussein Hamza

      Affiliations

    • Department of Epidemiology and Biostatistics, Erasmus MC, Rotterdam, The Netherlands
    • ,
  • Hans C. van Houwelingen

      Affiliations

    • Department of Medical Statistics & Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands
    • ,
  • Majanka H. Heijenbrok-Kal

      Affiliations

    • Department of Epidemiology and Biostatistics, Erasmus MC, Rotterdam, The Netherlands
    • Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
    • ,
  • Theo Stijnen

      Affiliations

    • Department of Medical Statistics & Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands
    • Corresponding Author InformationCorresponding author. Department of Medical Statistics & Bioinformatics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands. Tel.: +31-715269701; fax: +31-715268280.

Accepted 3 February 2009. published online 24 April 2009.

Abstract 

Objective

To show how the bivariate random effects meta-analysis model can be used to study the relation between the explanatory variables and the performance of diagnostic tests as characterized by a summary receiver operating characteristic curve (SROCC).

Study Design and Setting

The subject is discussed by means of a data example in which sensitivity and specificity are available for 149 studies on one of three tests for the diagnosis of coronary artery disease. The focus is on comparing SROCCs between different tests adjusted for potential confounders, but the methods can be applied much more generally.

Results

Different types of SROCCs can be calculated. The influence of explanatory variables on an SROCC is an ensemble of sensitivity and specificity regression coefficients and covariance parameters. The regression coefficients of the SROCC are estimated and tested, and the percentage explained variability is determined. Under certain assumptions, the SROCCs of different covariate values do not cross. If these are fulfilled, it is much easier to describe the influence of explanatory variables. Conclusions can depend on the type of SROCC.

Conclusion

The bivariate random effects meta-analysis model is an appropriate and convenient framework to investigate the effect of covariates on the performance of diagnostic tests as measured by SROCCs.

Keywords: Diagnostic meta-analysis, Summary ROC curves, Metaregression, Bivariate random effects model, Sensitivity, Specificity

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PII: S0895-4356(09)00057-2

doi:10.1016/j.jclinepi.2009.02.002

Journal of Clinical Epidemiology
Volume 62, Issue 12 , Pages 1284-1291, December 2009

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