A Tutorial for Meta-Analysis of Diagnostic Tests for Low-Prevalence Diseases: Bayesian Models and Software

Authors

  • Johny J. Pambabay-Calero
    Faculty of Natural Sciences and Mathematics, ESPOL, Polytechnic University, Guayaquil, Ecuador
  • Sergio A. Bauz-Olvera
    Faculty of Natural Sciences and Mathematics, ESPOL, Polytechnic University, Guayaquil, Ecuador
  • Ana B. Nieto-Librero
    Department of Statistics, University of Salamanca, Salamanca, Spain
  • Maria Purificación Galindo-Villardón
    Department of Statistics, University of Salamanca, Salamanca, Spain
  • Ana B. Sánchez-García
    INICO, Faculty of Education, University of Salamanca, Salamanca, Spain

Abstract

Although measures such as sensitivity and specificity are used in the study of diagnostic test accuracy, these are not appropriate for integrating heterogeneous studies. Therefore, it is essential to assess in detail all related aspects prior to integrating a set of studies so that the correct model can then be selected. This work describes the scheme employed for making decisions regarding the use of the R, STATA and SAS statistical programs. We used the R Program Meta-Analysis of Diagnostic Accuracy package for determining the correlation between sensitivity and specificity. This package considers fixed, random and mixed effects models and provides excellent summaries and assesses heterogeneity. For selecting various cutoff points in the meta-analysis, we used the STATA module for meta-analytical integration of diagnostic test accuracy studies, which produces bivariate outputs for heterogeneity.