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.

References 

  1. Littenberg B, Moses LE. Estimating diagnostic accuracy from multiple conflicting reports—a new meta-analytic method. Med Decis Making. 1993;13:313–321
  2. Moses LE, Shapiro D, Littenberg B. Combining independent studies of a diagnostic test into a summary ROC curve: data-analytic approaches and some additional considerations. Stat Med. 1993;12:1293–1316
  3. Rutter CM, Gatsonis CA. A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat Med. 2001;20:2865–2884
  4. Reitsma JB, Glas AS, Rutjes AWS, Scholten RJPM, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol. 2005;58:982–990
  5. Arends LR, Hamza TH, Houwelingen van JC, Heijenbrok-Kal MH, Hunink MGM, Stijnen T. Multivariate random effects meta-analysis of ROC curves. Med Decis Making. 2008;28:621–638
  6. Chu H, Cole SR. Bivariate meta-analysis for sensitivity and specificity with sparse data: a generalized linear mixed model approach. J Clin Epidemiol. 2006;59:1331—31
  7. Harbord RM, Deeks JJ, Egger M, Whiting P, Sterne JA. A unification of models for meta-analysis of diagnostic accuracy studies. Biostatistics. 2006;1:1–21
  8. Houwelingen van JC, Zwinderman AH, Stijnen T. A bivariate look at meta-analysis. Stat Med. 1993;12:2273–2284
  9. Houwelingen van JC, Arends LR, Stijnen T. Advanced methods in meta-analysis: multivariate approach and meta-regression. Stat Med. 2002;21:589–624
  10. Heijenbrok-Kal MH, Hunink MGM, Fleischmann KE. Stress echocardiography, stress SPECT and electron-beam computed tomography for the assessment of coronary artery disease: a meta-analysis of diagnostic performance. Am Heart J. 2007;154:415–423
  11. Hamza TH, van Houwelingen HC, Stijnen T. Random effects meta-analysis of proportions: The binomial distribution should be used to model the within study variability. J Clin Epidemiol. 2008;61:41–51
  12. Hamza TH, Reitsma JB, Stijnen T. Meta-analysis of diagnostic studies: a comparison of random intercept, normal-normal and binomial-normal bivariate summary ROC approaches. Med Decis Making. 2008;28:639–649
  13. Walter SD, Jadad AR. Meta-analysis of screening data: a survey of the literature. Stat Med. 1999;18:3409–3424
  14. Lord FM. A paradox in the interpretation of group comparisons. Psychol Bull. 1967;68:304–305

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

Article Tools

* Image Usage & Resolution