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Meta-Analysis in DTA with Hierarchical Models Bivariate and HSROC: Simulation Study

  • Sergio A. Bauz-OlveraEmail author
  • Johny J. Pambabay-Calero
  • Ana B. Nieto-Librero
  • Ma. Purificación Galindo-Villardón
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 301)

Abstract

In meta-analysis for diagnostic test accuracy (DTA), summary measures such as sensitivity, specificity, and odds ratio are used. However, these measures may not be adequate to integrate studies with low prevalence, which is why statistical modeling based on true positives and false positives is necessary. In this context, there are several statistical methods, the first of which is a bivariate random effects model, part of the assumption that the logit of sensitivity and specificity follow a bivariate normal distribution, the second, refers to the HSROC or hierarchical model, is similar to bivariate, with the particularity that it directly models the sensitivity and specificity relationship through cut points. Using simulations, we investigate the performance of hierarchical models, varying their parameters and hyperparameters and proposing a better management of variability within and between studies. The results of the simulated data are analyzed according to the criterion of adjustment of the models and estimates of their parameters.

Keywords

Diagnostic precision Meta-analysis HSROC model Bivariate model Low prevalence 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sergio A. Bauz-Olvera
    • 1
    Email author
  • Johny J. Pambabay-Calero
    • 2
  • Ana B. Nieto-Librero
    • 3
    • 4
  • Ma. Purificación Galindo-Villardón
    • 3
    • 4
  1. 1.Facultad de Ciencias de la VidaEscuela Superior Politécnica del LitoralGuayaquilEcuador
  2. 2.Facultad de Ciencias Naturales y MatemáticasEscuela Superior Politécnica del LitoralGuayaquilEcuador
  3. 3.Dpto. de Estadística, Facultad de MedicinaUniversidad de SalamancaSalamancaSpain
  4. 4.Instituto de Investigación Biomédica (IBSAL)SalamancaSpain

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