Skip to main content
SpringerLink
Log in

Meta-Analysis Methods of Diagnostic Test Accuracy Studies

  • Protocol
  • First Online:
Meta-Research

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2345))

Abstract

Meta-analytic techniques are used to combine the results of different studies that have evaluated the accuracy of diagnostic tests. In this article, we present univariate and multivariate meta-analysis methods for a single test and we provide an extensive description of methods for meta-analysis and comparison of multiple diagnostic tests. We close with a practical example of a meta-analysis that aimed to determine whether Rheumatoid Factor identifies patients with Rheumatoid Arthritis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 149.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Mandrekar JN (2010) Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol 5(9):1315–1316. https://doi.org/10.1097/JTO.0b013e3181ec173d

    Article  PubMed  Google Scholar 

  2. Arends LR, Hamza TH, van Houwelingen JC, Heijenbrok-Kal MH, Hunink MG, Stijnen T (2008) Bivariate random effects meta-analysis of ROC curves. Med Decis Making 28(5):621–638. https://doi.org/10.1177/0272989X08319957

    Article  CAS  PubMed  Google Scholar 

  3. Harbord RM, Deeks JJ, Egger M, Whiting P, Sterne JA (2007) A unification of models for meta-analysis of diagnostic accuracy studies. Biostatistics 8(2):239–251. https://doi.org/10.1093/biostatistics/kxl004

    Article  PubMed  Google Scholar 

  4. Macaskill P (2004) 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. https://doi.org/10.1016/j.jclinepi.2003.12.019

    Article  PubMed  Google Scholar 

  5. Moses LE, Shapiro D, Littenberg B (1993) 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. https://doi.org/10.1002/sim.4780121403

    Article  CAS  PubMed  Google Scholar 

  6. Rutter CM, Gatsonis CA (2001) A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat Med 20(19):2865–2884. https://doi.org/10.1002/sim.942

    Article  CAS  PubMed  Google Scholar 

  7. Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM (2003) The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol 56(11):1129–1135. https://doi.org/10.1016/s0895-4356(03)00177-x

    Article  PubMed  Google Scholar 

  8. Littenberg B, Moses LE (1993) Estimating diagnostic accuracy from multiple conflicting reports: a new meta-analytic method. Med Decis Making 13(4):313–321. https://doi.org/10.1177/0272989X9301300408

    Article  CAS  PubMed  Google Scholar 

  9. Ma X, Nie L, Cole SR, Chu H (2016) Statistical methods for multivariate meta-analysis of diagnostic tests: an overview and tutorial. Stat Methods Med Res 25(4):1596–1619. https://doi.org/10.1177/0962280213492588

    Article  PubMed  Google Scholar 

  10. Chu H, Cole SR (2006) Bivariate meta-analysis of sensitivity and specificity with sparse data: a generalized linear mixed model approach. J Clin Epidemiol 59(12):1331–1332.; ; author reply 1332-1333. https://doi.org/10.1016/j.jclinepi.2006.06.011

    Article  PubMed  Google Scholar 

  11. Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH (2005) Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol 58(10):982–990. https://doi.org/10.1016/j.jclinepi.2005.02.022

    Article  PubMed  Google Scholar 

  12. Berkey CS, Hoaglin DC, Antczak-Bouckoms A, Mosteller F, Colditz GA (1998) Meta-analysis of multiple outcomes by regression with random effects. Stat Med 17(22):2537–2550. https://doi.org/10.1002/(sici)1097-0258(19981130)17:22<2537::aid-sim953>3.0.co;2-c

    Article  CAS  PubMed  Google Scholar 

  13. van Houwelingen HC, Arends LR, Stijnen T (2002) Advanced methods in meta-analysis: multivariate approach and meta-regression. Stat Med 21(4):589–624. https://doi.org/10.1002/sim.1040

    Article  PubMed  Google Scholar 

  14. Hamza TH, van Houwelingen HC, Stijnen T (2008) The binomial distribution of meta-analysis was preferred to model within-study variability. J Clin Epidemiol 61(1):41–51. https://doi.org/10.1016/j.jclinepi.2007.03.016

    Article  PubMed  Google Scholar 

  15. Chen Y, Liu Y, Ning J, Nie L, Zhu H, Chu H (2017) A composite likelihood method for bivariate meta-analysis in diagnostic systematic reviews. Stat Methods Med Res 26(2):914–930. https://doi.org/10.1177/0962280214562146

    Article  PubMed  Google Scholar 

  16. Chu H, Nie L, Cole SR, Poole C (2009) Meta-analysis of diagnostic accuracy studies accounting for disease prevalence: alternative parameterizations and model selection. Stat Med 28(18):2384–2399. https://doi.org/10.1002/sim.3627

    Article  PubMed  Google Scholar 

  17. Chen Y, Liu Y, Ning J, Cormier J, Chu H (2015) A hybrid model for combining case-control and cohort studies in systematic reviews of diagnostic tests. J R Stat Soc Ser C Appl Stat 64(3):469–489. https://doi.org/10.1111/rssc.12087

    Article  PubMed  Google Scholar 

  18. Ma X, Chen Y, Cole SR, Chu H (2016) A hybrid Bayesian hierarchical model combining cohort and case-control studies for meta-analysis of diagnostic tests: accounting for partial verification bias. Stat Methods Med Res 25(6):3015–3037. https://doi.org/10.1177/0962280214536703

    Article  PubMed  Google Scholar 

  19. Suzuki S, Moro-oka T, Choudhry NK (2004) The conditional relative odds ratio provided less biased results for comparing diagnostic test accuracy in meta-analyses. J Clin Epidemiol 57(5):461–469. https://doi.org/10.1016/j.jclinepi.2003.09.017

    Article  PubMed  Google Scholar 

  20. Siadaty MS, Philbrick JT, Heim SW, Schectman JM (2004) Repeated-measures modeling improved comparison of diagnostic tests in meta-analysis of dependent studies. J Clin Epidemiol 57(7):698–711. https://doi.org/10.1016/j.jclinepi.2003.12.007

    Article  PubMed  Google Scholar 

  21. Dimou NL, Adam M, Bagos PG (2016) A multivariate method for meta-analysis and comparison of diagnostic tests. Stat Med 35(20):3509–3523. https://doi.org/10.1002/sim.6919

    Article  PubMed  Google Scholar 

  22. Nyaga VN, Aerts M, Arbyn M (2018) ANOVA model for network meta-analysis of diagnostic test accuracy data. Stat Methods Med Res 27(6):1766–1784. https://doi.org/10.1177/0962280216669182

    Article  PubMed  Google Scholar 

  23. Nyaga VN, Arbyn M, Aerts M, NN V, Arbyn M, Aerts M (2018) Beta-binomial analysis of variance model for network meta-analysis of diagnostic test accuracy data. Stat Methods Med Res 27(8):2554–2566. https://doi.org/10.1177/0962280216682532

    Article  Google Scholar 

  24. Trikalinos TA, Hoaglin DC, Small KM, Terrin N, Schmid CH (2014) Methods for the joint meta-analysis of multiple tests. Res Synth Methods 5(4):294–312. https://doi.org/10.1002/jrsm.1115

    Article  PubMed  Google Scholar 

  25. Hoyer A, Kuss O (2018) Meta-analysis for the comparison of two diagnostic tests to a common gold standard: a generalized linear mixed model approach. Stat Methods Med Res 27(5):1410–1421. https://doi.org/10.1177/0962280216661587

    Article  PubMed  Google Scholar 

  26. Chu H, Chen S, Louis TA (2009) Random effects models in a meta-analysis of the accuracy of two diagnostic tests without a gold standard. J Am Stat Assoc 104(486):512–523. https://doi.org/10.1198/jasa.2009.0017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Dendukuri N, Schiller I, Joseph L, Pai M (2012) Bayesian meta-analysis of the accuracy of a test for tuberculous pleuritis in the absence of a gold standard reference. Biometrics 68(4):1285–1293. https://doi.org/10.1111/j.1541-0420.2012.01773.x

    Article  PubMed  PubMed Central  Google Scholar 

  28. Enoe C, Georgiadis MP, Johnson WO (2000) Estimation of sensitivity and specificity of diagnostic tests and disease prevalence when the true disease state is unknown. Prev Vet Med 45(1–2):61–81. https://doi.org/10.1016/s0167-5877(00)00117-3

    Article  CAS  PubMed  Google Scholar 

  29. Gardner IA, Stryhn H, Lind P, Collins MT (2000) Conditional dependence between tests affects the diagnosis and surveillance of animal diseases. Prev Vet Med 45(1–2):107–122. https://doi.org/10.1016/s0167-5877(00)00119-7

    Article  CAS  PubMed  Google Scholar 

  30. Hui SL, Zhou XH (1998) Evaluation of diagnostic tests without gold standards. Stat Methods Med Res 7(4):354–370. https://doi.org/10.1177/096228029800700404

    Article  CAS  PubMed  Google Scholar 

  31. Menten J, Lesaffre E (2015) A general framework for comparative Bayesian meta-analysis of diagnostic studies. BMC Med Res Methodol 15:70. https://doi.org/10.1186/s12874-015-0061-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Nishimura K, Sugiyama D, Kogata Y, Tsuji G, Nakazawa T, Kawano S, Saigo K, Morinobu A, Koshiba M, Kuntz KM, Kamae I, Kumagai S (2007) Meta-analysis: diagnostic accuracy of anti-cyclic citrullinated peptide antibody and rheumatoid factor for rheumatoid arthritis. Ann Intern Med 146(11):797–808. https://doi.org/10.7326/0003-4819-146-11-200706050-00008

    Article  PubMed  Google Scholar 

  33. Bagos PG (2015) Meta-analysis in Stata using gllamm. Res Synth Methods 6(4):310–332. https://doi.org/10.1002/jrsm.1157

    Article  PubMed  Google Scholar 

  34. White IR (2009) Multivariate random-effects meta-analysis. Stata J 9:40–56

    Article  Google Scholar 

  35. Harbord RM, Whiting P (2009) Metandi: meta-analysis of diagnostic accuracy using hierarchical logistic regression. Stata J 9:211–229

    Article  Google Scholar 

  36. Dwamena B (2007) MIDAS: Stata module for meta-analytical integration of diagnostic test accuracy studies. In statistical software components, S456880. Boston College, 140 commonwealth avenue, Chestnut Hill MA 02467 USA: Boston College Department of economics

    Google Scholar 

Download references

Disclaimer

Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/World Health Organization.

Author information

Authors and Affiliations

  1. Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France

    Niki Dimou

  2. Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece

    Pantelis Bagos

Authors
  1. Niki Dimou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pantelis Bagos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Niki Dimou .

Editor information

Editors and Affiliations

  1. Department of Hygiene and Epidemiology University of Ioannina Medical School, Department of Epidemiology and Biostatistics Imperial College London London, UK, Ioannina, Greece

    Evangelos Evangelou

  2. Knowledge Translation Program Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Department of Surgery and Cancer Faculty of Medicine Institute of Reproductive and Developmental Biology Imperial College London, UK ISSN, Toronto, ON, Canada

    Areti Angeliki Veroniki

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Dimou, N., Bagos, P. (2022). Meta-Analysis Methods of Diagnostic Test Accuracy Studies. In: Evangelou, E., Veroniki, A.A. (eds) Meta-Research. Methods in Molecular Biology, vol 2345. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1566-9_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1566-9_11

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1565-2

  • Online ISBN: 978-1-0716-1566-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation