Abstract
The rapid increase in diagnostic and screening techniques has urged the need to choose among multiple diagnostic tests. For the majority of diseases, there is more than a single test available, and studies usually compare a subset of these tests. In such cases, a separate meta-analysis of each test cannot provide a reliable answer on the relative accuracy of the multiple available tests. Extensions of standard (hierarchical) meta-analysis to network meta-analysis (NMA) models for the comparison of at least three diagnostic tests have been the subject of methodological research in recent years. NMA can be used to jointly analyze the totality of evidence in order to provide estimates of relative accuracy (sensitivity and specificity ), to compare tests that have not been compared head-to-head, and to obtain a ranking of all competing tests in order to further facilitate the decision-making process.
In this chapter, we illustrate current methodology for meta-analysis of multiple test comparisons, introduce NMA methods of diagnostic tests as an extension to the standard meta-analysis of diagnostic test accuracy (DTA) studies, and present existing approaches to rank tests according to their accuracy, specificity , and sensitivity . We also describe the basic concepts, underlying assumptions, and challenges in NMA of multiple diagnostic tests.
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References
Macaskill P GC, Deeks J, Harbord RM, Takwoingi Y. (2010) Chapter 10: Analysing and presenting results. In: Deeks JJ, Bossuyt PM, Gatsonis C (editors), Cochrane handbook for systematic reviews of diagnostic test accuracy version 1.0. The Cochrane Collaboration. http://srdta.cochrane.org/
Reitsma JB, Glas AS, Rutjes AW et al (2005) Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol 58:982–990
Rutter CM, Gatsonis CA (2001) A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat Med 20:2865–2884
Chu H, Cole SR (2006) Bivariate meta-analysis of sensitivity and specificity with sparse data: a generalized linear mixed model approach. J Clin Epidemiol 59:1331–1332
Arends LR, Hamza TH, Van Houwelingen JC et al (2008) Bivariate random effects meta-analysis of ROC curves. Med Decis Mak 28:621–638
Harbord RM, Deeks JJ, Egger M et al (2007) A unification of models for meta-analysis of diagnostic accuracy studies. Biostatistics 8:239–251
Takwoingi Y, Leeflang MMG, Deeks JJ (2013) Empirical evidence of the importance of comparative studies of diagnostic test accuracy. Ann Intern Med 158:544–554
Agency for Healthcare Research and Quality (AHRQ) (2007) Methods reference guide for effectiveness and comparative effectiveness reviews
Canadian Agency for Drugs and Technologies in Health (CADTH) Guidelines for the Economic Evaluation of Health Technologies: Canada. www.cadth.ca/media/pdf/186_EconomicGuidelines_e.pdf
Dias S, Welton NJ, Sutton AJ et al (2013) Evidence synthesis for decision making 1: introduction. Med Decis Mak 33:597–606
Dias S, Welton NJ, Sutton AJ et al (2013) Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials. Med Decis Mak 33:641–656
Indirect Comparisons Working Group (ISWG) Report of the Indirect Comparisons working Group to the Pharmaceutical Benefits Advisory Committee: Assessing indirect comparisons. www.health.gov.au/internet/main/publishing.nsf/Content/B11E8EF19B358E39CA25754B000A9C07/$File/ICWG%20Report%20FINAL2.pdf
National Institute for Health and Clinical Excellence (NICE) Guide to the Methods of Technology Appraisal. www.nice.org.uk/media/B52/A7/TAMethodsGuideUpdatedJune2008.pdf
Dimou NL, Adam M, Bagos PG (2016) A multivariate method for meta-analysis and comparison of diagnostic tests. Stat Med 35:3509–3523
Hoyer A, Kuss O (2018) Meta-analysis for the comparison of two diagnostic tests-a new approach based on copulas. Stat Med 37:739–748
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 27:1410–1421
Menten J, Lesaffre E (2015) A general framework for comparative Bayesian meta-analysis of diagnostic studies. BMC Med Res Methodol 15:70
Nyaga VN, Aerts M, Arbyn M (2018) ANOVA model for network meta-analysis of diagnostic test accuracy data. Stat Methods Med Res 27:1766–1784
Nyaga VN, Arbyn M, Aerts M (2018) Beta-binomial analysis of variance model for network meta-analysis of diagnostic test accuracy data. Stat Methods Med Res 27:2554–2566
Trikalinos TA, Hoaglin DC, Small KM et al (2014) Methods for the joint meta-analysis of multiple tests. Res Synth Methods 5:294–312
Rücker G (2018) Network meta-analysis of diagnostic test accuracy studies. In: Biondi-Zoccai G (ed) Diagnostic Meta-Analysis. Springer, Cham
Salanti G (2012) Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Res Synth Methods 3:80–97
Veroniki AA, Vasiliadis HS, Higgins JP et al (2013) Evaluation of inconsistency in networks of interventions. Int J Epidemiol 42:332–345
Takwoingi Y, Partlett C, Riley RD et al (2020) Methods and reporting of systematic reviews of comparative accuracy were deficient: a methodological survey and proposed guidance. J Clin Epidemiol 121:1–14
Mavridis D, Salanti G (2013) A practical introduction to multivariate meta-analysis. Stat Methods Med Res 22:133–158
Riley RD, Price MJ, Jackson D et al (2015) Multivariate meta-analysis using individual participant data. Res Synth Methods 6:157–174
Riley RD, Thompson JR, Abrams KR (2008) An alternative model for bivariate random-effects meta-analysis when the within-study correlations are unknown. Biostatistics 9:172–186
Koliopoulos G, Nyaga VN, Santesso N et al (2017) Cytology versus HPV testing for cervical cancer screening in the general population. Cochrane Database Syst Rev 8(8):CD008587
Rabe-Hesketh S, Skrondal A (2008) Multilevel and longitudinal modeling using Stata. Stata Press, College Station, TX
Chang SM (2012) methods guide for medical test reviews. https://www.ncbi.nlm.nih.gov/books/NBK98241/ (ed)NCBI, Rockville, MD
Trikalinos TA, Balion CM, Coleman CI et al (2012) Meta-analysis of test performance when there is a “gold standard”. In: Chang SM, Matchar DB, Smetana GW, Umscheid CA (eds) Methods guide for medical test reviews. Agency for Healthcare Research and Quality (US), Rockville (MD)
Ma X (2015) Statistical methods for multivariate meta-analysis of diagnostic tests. University of Minessota. https://conservancy.umn.edu/handle/11299/175241
Cheng W (2016) Network meta-analysis of diagnostic accuracy studies. Brown University. https://repository.library.brown.edu/studio/item/bdr:674079/
Owen RK, Cooper NJ, Quinn TJ et al (2018) Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for health care policy and decision-making. J Clin Epidemiol 99:64–74
Lu G, Ades AE (2004) Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med 23:3105–3124
Higgins JP, Jackson D, Barrett JK et al (2012) Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res Synth Methods 3:98–110
Lu G, Ades AE (2006) Assessing evidence inconsistency in mixed treatment comparisons. J Am Stat Assoc 101:447–459
Jansen JP, Trikalinos T, Cappelleri JC et al (2014) Indirect treatment comparison/network meta-analysis study questionnaire to assess relevance and credibility to inform health care decision making: an ISPOR-AMCP-NPC good practice task force report. Value Health 17:157–173
Deutsch R, Mindt M, Xu R et al (2009) Quantifying relative superiority among many binary-valued diagnostic tests in the presence of a gold standard. J Data Sci 7:161–177
Acknowledgments
GR was funded by DFG—German Research Foundation—grant number RU1747/1-2. YT is funded by a National Institute for Health Research (NIHR) Postdoctoral Fellowship. The views expressed are those of the authors and not necessarily those of the National Health Services (NHS), the NIHR, or the Department of Health and Social Care. We thank Dr. Antonios Athanasiou for his help on preparing the data for our illustrative example.
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Veroniki, A.A., Tsokani, S., Rücker, G., Mavridis, D., Takwoingi, Y. (2022). Challenges in Comparative Meta-Analysis of the Accuracy of Multiple Diagnostic Tests. 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_18
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DOI: https://doi.org/10.1007/978-1-0716-1566-9_18
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