Advertisement

Meta-Analysis of Diagnostic Test Accuracy Studies

  • Guido Schwarzer
  • James R. Carpenter
  • Gerta Rücker
Part of the Use R! book series (USE R)

Abstract

Meta-analysis of diagnostic test accuracy (DTA) studies differs from meta-analysis of intervention studies in a number of respects. In this chapter, we explain the issues raised by meta-analysis of diagnostic accuracy studies and how these may be addressed. Alongside the statistical models, we present the R package mada [5] written for fitting these models and graphing the results.

Keywords

Receiver Operating Characteristic Curve Negative Likelihood Ratio Diagnostic Odds Ratio Bivariate Model Diagnostic Test Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Arends, L.R., Hamza, T.H., van Houwelingen, J., Heijenbrok-Kal, M., Hunink, M., Stijnen, T.: Bivariate random effects meta-analysis of ROC curves. Med. Decis. Making 28(5), 621–638 (2008)CrossRefGoogle Scholar
  2. 2.
    Burnham, K.P., Anderson, D.R.: Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Springer, New York (2002)Google Scholar
  3. 3.
    Chu, H., Cole, S.R.: Bivariate meta-analysis of sensitivity and specificity with sparse data: a generalized linear mixed approach. J. Clin. Epidemiol. 59, 1331–1333 (2006)CrossRefGoogle Scholar
  4. 4.
    Chu, H., Guo, H.: Letter to the editor. Biostatistics 10(1), 201–203 (2009)CrossRefGoogle Scholar
  5. 5.
    Doebler, P.: mada: Meta-analysis of diagnostic accuracy (2014). http://www.cran.R-project.org/package=mada. R package version 0.5.6
  6. 6.
    Doebler, P., Holling, H., Böhning, D.: A mixed model approach to meta-analysis of diagnostic studies with binary test outcome. Psychol. Methods 17(3), 418–436 (2012). doi: 10.1037/a0028091 CrossRefGoogle Scholar
  7. 7.
    Hamza, T.H., van Houwelingen, H.C., Stijnen, T.: Random effects meta-analysis of proportions: the binomial distribution should be used to model the within-study variability. J. Clin. Epidemiol. 61(1), 41–51 (2007). doi: 10.1016/j.jclinepi.2007.03.016 CrossRefGoogle Scholar
  8. 8.
    Harbord, R.M., Deeks, J.J., Egger, M., Whiting, P., Sterne, J.A.: A unification of models for meta-analysis of diagnostic accuracy studies. Biostatistics 8, 239–251 (2007)zbMATHCrossRefGoogle Scholar
  9. 9.
    Holling, H., Böhning, W., Böhning, D.: Meta-analysis of diagnostic studies based upon SROC-curves: a mixed model approach using the lehmann family. Stat. Model. 12(4), 347–375 (2012)MathSciNetCrossRefGoogle Scholar
  10. 10.
    van Houwelingen, H.C., Zwinderman, K.H., Stijnen, T.: A bivariate approach to meta-analysis. Stat. Med. 12, 2273–2284 (1993)CrossRefGoogle Scholar
  11. 11.
    Macaskill, P.: Empirical Bayes estimates generated in a hierarchical summary ROC analysis agreed closely with those of a full Bayesian analysis. J. Clin. Epidemiol. 57(9), 925–932 (2004)CrossRefGoogle Scholar
  12. 12.
    Moses, L., 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. 12(14), 1293–1316 (1993)CrossRefGoogle Scholar
  13. 13.
    Pepe, M.S.: The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press, Oxford (2004)zbMATHGoogle Scholar
  14. 14.
    Reitsma, J., Glas, A., Rutjes, A., Scholten, R., Bossuyt, P., Zwinderman, A.: Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J. Clin. Epidemiol. 58(10), 982–990 (2005)CrossRefGoogle Scholar
  15. 15.
    Rücker, G., Schumacher, M.: Letter to the editor. Biostatistics 10(4), 806–807 (2009)CrossRefGoogle Scholar
  16. 16.
    Rücker, G., Schumacher, M.: Summary ROC curve based on the weighted Youden index for selecting an optimal cutpoint in meta-analysis of diagnostic accuracy. Stat. Med. 29, 3069–3078 (2010)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Rutter, C.M., Gatsonis, C.A.: A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat. Med. 20, 2865–2884 (2001)CrossRefGoogle Scholar
  18. 18.
    Scheidler, J., Hricak, H., Yu, K., Subak, L., Segal, M.: Radiological evaluation of lymph node metastases in patients with cervical cancer. A meta-analysis. JAMA 278(13), 1096–1101 (1997)Google Scholar
  19. 19.
    Walter, S.D.: Properties of the summary receiver operating characteristic (ROC) curve for diagnostic test data. Stat. Med. 21, 1237–1256 (2002)CrossRefGoogle Scholar
  20. 20.
    Willis, B.H., Quigley, M.: Uptake of newer methodological developments and the deployment of meta-analysis in diagnostic test research: a systematic review. BMC Med. Res. Methodol. 11, 27 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Guido Schwarzer
    • 1
  • James R. Carpenter
    • 2
  • Gerta Rücker
    • 1
  1. 1.Institute for Medical Biometry and StatisticsMedical Center – University of FreiburgFreiburgGermany
  2. 2.MRC Clinical Trials UnitLondon and London School of Hygiene and Tropical MedicineLondonUK

Personalised recommendations