When are summary ROC curves appropriate for diagnostic meta-analyses?

Stat Med. 2009 Sep 20;28(21):2653-68. doi: 10.1002/sim.3631.


Diagnostic tests are increasingly evaluated with systematic reviews and this has lead to the recent developments of statistical methods to analyse such data. The most commonly used method is the summary receiver operating characteristic (SROC) curve, which can be fitted with a non-linear bivariate random-effects model. This paper focuses on the practical problems of interpreting and presenting data from such analyses. First, many meta-analyses may be underpowered to obtain reliable estimates of the SROC parameters. Second, the SROC model may be inappropriate. In these situations, a summary with two univariate meta-analyses of the true and false positive rates (TPRs and FPRs) may be more appropriate. We characterize the type of problems that can occur in fitting these models and present an algorithm to guide the analyst of such studies, with illustrations from analyses of published data. A set of R functions, freely available to perform these analyses, can be downloaded from (www.diagmeta.info).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Binomial Distribution
  • Confidence Intervals
  • Diagnostic Tests, Routine*
  • Meta-Analysis as Topic*
  • ROC Curve*