Empirical comparisons of meta-analysis methods for diagnostic studies: a meta-epidemiological study

Kristine J. Rosenberger, Haitao Chu, Lifeng Lin

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


OBJECTIVES: Several methods are commonly used for meta-analyses of diagnostic studies, such as the bivariate linear mixed model (LMM). It estimates the overall sensitivity, specificity, their correlation, diagnostic OR (DOR) and the area under the curve (AUC) of the summary receiver operating characteristic (ROC) estimates. Nevertheless, the bivariate LMM makes potentially unrealistic assumptions (ie, normality of within-study estimates), which could be avoided by the bivariate generalised linear mixed model (GLMM). This article aims at investigating the real-world performance of the bivariate LMM and GLMM using meta-analyses of diagnostic studies from the Cochrane Library.

METHODS: We compared the bivariate LMM and GLMM using the relative differences in the overall sensitivity and specificity, their 95% CI widths, between-study variances, and the correlation between the (logit) sensitivity and specificity. We also explored their relationships with the number of studies, number of subjects, overall sensitivity and overall specificity.

RESULTS: Among the extracted 1379 meta-analyses, point estimates of overall sensitivities and specificities by the bivariate LMM and GLMM were generally similar, but their CI widths could be noticeably different. The bivariate GLMM generally produced narrower CIs than the bivariate LMM when meta-analyses contained 2-5 studies. For meta-analyses with <100 subjects or the overall sensitivities or specificities close to 0% or 100%, the bivariate LMM could produce substantially different AUCs, DORs and DOR CI widths from the bivariate GLMM.

CONCLUSIONS: The variation of estimates calls into question the appropriateness of the normality assumption within individual studies required by the bivariate LMM. In cases of notable differences presented in these methods' results, the bivariate GLMM may be preferred.

Original languageEnglish (US)
Pages (from-to)e055336
JournalBMJ open
Issue number5
StatePublished - May 9 2022

Bibliographical note

Publisher Copyright:
© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.


  • epidemiology
  • general medicine (see internal medicine)
  • medical education & training
  • statistics & research methods
  • Epidemiologic Studies
  • Meta-Analysis as Topic
  • Humans
  • Sensitivity and Specificity
  • Linear Models
  • ROC Curve

PubMed: MeSH publication types

  • Journal Article


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