Literature DB >> 34698503

QUADAS-C: A Tool for Assessing Risk of Bias in Comparative Diagnostic Accuracy Studies.

Bada Yang1, Sue Mallett2, Yemisi Takwoingi3, Clare F Davenport3, Christopher J Hyde4, Penny F Whiting5, Jonathan J Deeks3, Mariska M G Leeflang1, Patrick M M Bossuyt, Miriam G Brazzelli, Jacqueline Dinnes, Kurinchi S Gurusamy, Hayley E Jones, Stefan Lange, Miranda W Langendam, Petra Macaskill, Matthew D F McInnes, Johannes B Reitsma, Anne W S Rutjes, Alison Sinclair, Henrica C W de Vet, Gianni Virgili, Ros Wade, Marie E Westwood.   

Abstract

Comparative 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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Year:  2021        PMID: 34698503     DOI: 10.7326/M21-2234

Source DB:  PubMed          Journal:  Ann Intern Med        ISSN: 0003-4819            Impact factor:   25.391


  7 in total

Review 1.  Comparison of Diagnostic Test Accuracy of Cone-Beam Breast Computed Tomography and Digital Breast Tomosynthesis for Breast Cancer: A Systematic Review and Meta-Analysis Approach.

Authors:  Temitope Emmanuel Komolafe; Cheng Zhang; Oluwatosin Atinuke Olagbaju; Gang Yuan; Qiang Du; Ming Li; Jian Zheng; Xiaodong Yang
Journal:  Sensors (Basel)       Date:  2022-05-09       Impact factor: 3.847

2.  The Diagnostic Accuracy and Clinimetric Properties of Screening Instruments to Identify Frail Older Adults Attending Emergency Departments: A Protocol for a Mixed Methods Systematic Review and Meta-Analysis.

Authors:  Elizabeth Moloney; Duygu Sezgin; Mark O'Donovan; Kadjo Yves Cedric Adja; Keith McGrath; Aaron Liew; Jacopo Lenzi; Davide Gori; Kieran O'Connor; David William Molloy; Evelyn Flanagan; Darren McLoughlin; Maria Pia Fantini; Suzanne Timmons; Rónán O'Caoimh
Journal:  Int J Environ Res Public Health       Date:  2022-01-26       Impact factor: 3.390

Review 3.  MRI detection of suspected nasopharyngeal carcinoma: a systematic review and meta-analysis.

Authors:  Vineet Vijay Gorolay; Naomi Natasha Niles; Ya Ruth Huo; Navid Ahmadi; Kate Hanneman; Elizabeth Thompson; Michael Vinchill Chan
Journal:  Neuroradiology       Date:  2022-04-30       Impact factor: 2.995

4.  TOMAS-R: A template to identify and plan analysis for clinically important variation and multiplicity in diagnostic test accuracy systematic reviews.

Authors:  Sue Mallett; Jacqueline Dinnes; Yemisi Takwoingi; Lavinia Ferrante de Ruffano
Journal:  Diagn Progn Res       Date:  2022-09-22

5.  Diagnostic performance of C-TIRADS in malignancy risk stratification of thyroid nodules: A systematic review and meta-analysis.

Authors:  Yan Hu; Shangyan Xu; Weiwei Zhan
Journal:  Front Endocrinol (Lausanne)       Date:  2022-09-08       Impact factor: 6.055

6.  Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis.

Authors:  He-Li Xu; Ting-Ting Gong; Fang-Hua Liu; Hong-Yu Chen; Qian Xiao; Yang Hou; Ying Huang; Hong-Zan Sun; Yu Shi; Song Gao; Yan Lou; Qing Chang; Yu-Hong Zhao; Qing-Lei Gao; Qi-Jun Wu
Journal:  EClinicalMedicine       Date:  2022-09-17

7.  A protocol for the VISION study: An indiVidual patient data meta-analysis of randomised trials comparing MRI-targeted biopsy to standard transrectal ultraSound guided bIopsy in the detection of prOstate cancer.

Authors:  Veeru Kasivisvanathan; Vinson Wai-Shun Chan; Keiran D Clement; Brooke Levis; Masoom Haider; Ridhi Agarwal; Mark Emberton; Gregory R Pond; Yemisi Takwoingi; Laurence Klotz; Caroline M Moore
Journal:  PLoS One       Date:  2022-02-03       Impact factor: 3.240

  7 in total

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