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Study designs for comparative diagnostic test accuracy: A methodological review and classification scheme.

Bada Yang1, Maria Olsen1, Yasaman Vali1

  • 1Department of Epidemiology and Data Science, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands.

Journal of Clinical Epidemiology
|April 29, 2021
PubMed
Summary

This study classified comparative diagnostic test accuracy (DTA) study designs, finding 46 unique combinations and five main categories. A standardized labeling system is proposed to improve clarity in DTA research.

Keywords:
BiasComparative accuracy studiesDiagnostic accuracyStudy designTest comparison

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Area of Science:

  • Medical research methodology
  • Diagnostic test accuracy studies

Background:

  • Comparative diagnostic test accuracy (DTA) studies are crucial for evaluating medical tests.
  • Variability in study designs and labeling complicates the interpretation and synthesis of DTA evidence.

Purpose of the Study:

  • To identify and classify diverse study designs used in comparative DTA research.
  • To analyze the labels authors use to describe these comparative DTA study designs.

Main Methods:

  • A methodological review of 100 comparative DTA studies published between 2015-2017 was conducted.
  • Six design elements characterizing participant flow were extracted to classify study designs.
  • Author-provided study design labels were also extracted and analyzed.

Main Results:

  • Forty-six unique combinations of study design features were identified.
  • Five main study design categories were established: 'fully paired', 'partially paired, nonrandom subset', 'unpaired randomized', and 'unpaired nonrandomized'.
  • Sixty-one studies used 29 unique labels, but only four accurately reflected specific comparative design features.

Conclusions:

  • A proposed classification scheme can aid systematic review authors in defining eligibility criteria and assessing risk of bias.
  • Developing a standardized labeling scheme for comparative DTA studies would enhance communication of design features and evidence strength.