title: The conditional relative odds ratio provided less biased results for comparing diagnostic test accuracy in meta-analyses.
abstract: OBJECTIVE
  Meta-analytic techniques are used to combine the results of different studies that have evaluated the accuracy of a given diagnostic test. The techniques commonly generate values that either describe the performance of a particular test or compare the discriminative ability of two tests. The later has received very little attention in the literature, and is the focus of this article.
  
  
  METHODS
  We summarize existing methods based on an odds ratio (OR) and propose a novel technique for conducting such analysis, the conditional relative odds ratio (CROR). We demonstrate how to extract the required data and calculate several different comparative indexes using a hypothetic example.
  
  
  RESULTS
  A paired analysis is preferred to decrease selection bias and increase statistical power. There is no standard method of obtaining the standard error (SE) of each relative OR; thus, the SE of the summary index might be underestimated under the assumption of no within-study variability.
  
  
  CONCLUSIONS
  The CROR method estimates less biased indexes with SEs, and conditioned on discordant results, it is much less problematic ethically and economically. However, small cell counts may lead to larger SEs, and it might be impossible to construct McNemar's 2 x 2 tables for some studies.
authors.affiliation: Department of Epidemiology, Harvard School of Public Heath, 677 Huntington Avenue, Boston, MA 02115, USA. ssuzuki@med.nagoya-cu.ac.jp
authors.forename: Sadao
authors.initials: S
authors.lastname: Suzuki
authors.forename: Takeo
authors.initials: T
authors.lastname: Moro-oka
authors.forename: Niteesh K
authors.initials: NK
authors.lastname: Choudhry
mesh_terms: D003627:Data Interpretation, Statistical; D003955:Diagnostic Tests, Routine; D006801:Humans; D016555:Matched-Pair Analysis; D015201:Meta-Analysis as Topic; D016017:Odds Ratio; D012107:Research Design; D015983:Selection Bias; D012680:Sensitivity and Specificity