Meta-analysis of full ROC curves using bivariate time-to-event models for interval-censored data.

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  • Author(s): Hoyer A;Hoyer A; Hirt S; Hirt S; Kuss O; Kuss O
  • Source:
    Research synthesis methods [Res Synth Methods] 2018 Mar; Vol. 9 (1), pp. 62-72. Date of Electronic Publication: 2017 Dec 20.
  • Publication Type:
    Journal Article
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  • Additional Information
    • Source:
      Publisher: Wiley Blackwell Country of Publication: England NLM ID: 101543738 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1759-2887 (Electronic) Linking ISSN: 17592879 NLM ISO Abbreviation: Res Synth Methods Subsets: MEDLINE
    • Publication Information:
      Publication: : Chichester : Wiley Blackwell
      Original Publication: Malden, MA : John Wiley & Sons, 2010-
    • Subject Terms:
    • Abstract:
      Systematic reviews and meta-analyses are the cornerstones of evidence-based medicine and inform treatment, diagnosis, or prevention of individual patients as well as policy decisions in health care. Statistical methods for the meta-analysis of intervention studies are well established today. Meta-analysis for diagnostic accuracy trials has also been a vivid research area in recent years, which is especially due to the increased complexity of their bivariate outcome of sensitivity and specificity. The situation is even more challenging when single studies report a full ROC curve with several pairs of sensitivity and specificity, each pair for a different threshold. Researchers frequently ignore this information and use only 1 pair of sensitivity and specificity from each study to arrive at meta-analytic estimates. Although methods to deal with the full information have been proposed, they have some disadvantages, eg, the numbers or values of thresholds have to be identical across studies, or the precise values of thresholds are ignored. We propose an approach for the meta-analysis of full ROC curves including the information from all thresholds by using bivariate time-to-event models for interval-censored data with random effects. This approach avoids the problems of previous methods and comes with the additional advantage that it allows for various distributions of the underlying continuous test values. The results from a small simulation study are given, which show that the approach works well in practice. Furthermore, we illustrate our new model using an example based on the population-based screening for type 2 diabetes mellitus.
      (Copyright © 2017 John Wiley & Sons, Ltd.)
    • Contributed Indexing:
      Keywords: ROC curve; interval-censored data; meta-analysis; time-to-event model
    • Publication Date:
      Date Created: 20171021 Date Completed: 20181029 Latest Revision: 20181029
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