• Bada Yang, Sue Mallett, Yemisi Takwoingi, Clare F Davenport, Christopher J Hyde, Penny F Whiting, Jonathan J Deeks, LeeflangMariska M GMMG0000-0001-5960-0471Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands (B.Y., M.M.L.)., QUADAS-C Group†, BossuytPatrick M MPMM, Miriam G Brazzelli, Jacqueline Dinnes, Kurinchi S Gurusamy, Hayley E Jones, Stefan Lange, Miranda W Langendam, Petra Macaskill, McInnesMatthew D FMDF, Johannes B Reitsma, RutjesAnne W SAWS, Alison Sinclair, de VetHenrica C WHCW, Gianni Virgili, Ros Wade, and Marie E Westwood.
• Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands (B.Y., M.M.L.).
• Ann. Intern. Med. 2021 Nov 1; 174 (11): 1592-1599.

AbstractComparative diagnostic test accuracy studies assess and compare the accuracy of 2 or more tests in the same study. Although these studies have the potential to yield reliable evidence regarding comparative accuracy, shortcomings in the design, conduct, and analysis may bias their results. The currently recommended quality assessment tool for diagnostic test accuracy studies, QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2), is not designed for the assessment of test comparisons. The QUADAS-C (Quality Assessment of Diagnostic Accuracy Studies-Comparative) tool was developed as an extension of QUADAS-2 to assess the risk of bias in comparative diagnostic test accuracy studies. Through a 4-round Delphi study involving 24 international experts in test evaluation and a face-to-face consensus meeting, an initial version of the tool was developed that was revised and finalized following a pilot study among potential users. The QUADAS-C tool retains the same 4-domain structure of QUADAS-2 (Patient Selection, Index Test, Reference Standard, and Flow and Timing) and comprises additional questions to each QUADAS-2 domain. A risk-of-bias judgment for comparative accuracy requires a risk-of-bias judgment for the accuracy of each test (resulting from QUADAS-2) and additional criteria specific to test comparisons. Examples of such additional criteria include whether participants either received all index tests or were randomly assigned to index tests, and whether index tests were interpreted with blinding to the results of other index tests. The QUADAS-C tool will be useful for systematic reviews of diagnostic test accuracy addressing comparative questions. Furthermore, researchers may use this tool to identify and avoid risk of bias when designing a comparative diagnostic test accuracy study.

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