Chapter 8: meta-analysis of test performance when there is a "gold standard"

J Gen Intern Med. 2012 Jun;27 Suppl 1(Suppl 1):S56-66. doi: 10.1007/s11606-012-2029-1.


Synthesizing information on test performance metrics such as sensitivity, specificity, predictive values and likelihood ratios is often an important part of a systematic review of a medical test. Because many metrics of test performance are of interest, the meta-analysis of medical tests is more complex than the meta-analysis of interventions or associations. Sometimes, a helpful way to summarize medical test studies is to provide a "summary point", a summary sensitivity and a summary specificity. Other times, when the sensitivity or specificity estimates vary widely or when the test threshold varies, it is more helpful to synthesize data using a "summary line" that describes how the average sensitivity changes with the average specificity. Choosing the most helpful summary is subjective, and in some cases both summaries provide meaningful and complementary information. Because sensitivity and specificity are not independent across studies, the meta-analysis of medical tests is fundamentaly a multivariate problem, and should be addressed with multivariate methods. More complex analyses are needed if studies report results at multiple thresholds for positive tests. At the same time, quantitative analyses are used to explore and explain any observed dissimilarity (heterogeneity) in the results of the examined studies. This can be performed in the context of proper (multivariate) meta-regressions.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Algorithms
  • Diagnostic Techniques and Procedures / standards*
  • Evidence-Based Medicine / methods
  • Evidence-Based Medicine / standards
  • Guidelines as Topic*
  • Humans
  • Meta-Analysis as Topic*
  • Outcome and Process Assessment, Health Care / methods
  • Outcome and Process Assessment, Health Care / standards
  • Predictive Value of Tests
  • Reference Standards
  • Review Literature as Topic*
  • Sensitivity and Specificity