A composite likelihood method for bivariate meta-analysis in diagnostic systematic reviews

Yong Chen, Yulun Liu, Jing Ning, Lei Nie, Hongjian Zhu, Haitao Chu

Research output: Contribution to journalReview articlepeer-review

17 Scopus citations

Abstract

Diagnostic systematic review is a vital step in the evaluation of diagnostic technologies. In many applications, it involves pooling pairs of sensitivity and specificity of a dichotomized diagnostic test from multiple studies. We propose a composite likelihood (CL) method for bivariate meta-analysis in diagnostic systematic reviews. This method provides an alternative way to make inference on diagnostic measures such as sensitivity, specificity, likelihood ratios, and diagnostic odds ratio. Its main advantages over the standard likelihood method are the avoidance of the nonconvergence problem, which is nontrivial when the number of studies is relatively small, the computational simplicity, and some robustness to model misspecifications. Simulation studies show that the CL method maintains high relative efficiency compared to that of the standard likelihood method. We illustrate our method in a diagnostic review of the performance of contemporary diagnostic imaging technologies for detecting metastases in patients with melanoma.

Original languageEnglish (US)
Pages (from-to)914-930
Number of pages17
JournalStatistical methods in medical research
Volume26
Issue number2
DOIs
StatePublished - Apr 1 2017

Bibliographical note

Funding Information:
Yong Chen was supported in part by grant number R03HS022900 from the Agency for Healthcare Research and Quality. Jing Ning was partially supported by start-up funds from the University of Texas MD Anderson Cancer Center. Haitao Chu was supported in part by the US NIAID AI103012, NCI P01CA142538, NCI P30CA077598, and U54-MD008620.

Keywords

  • Bivariate generalized linear mixed effects model
  • composite likelihood
  • diagnostic accuracy
  • diagnostic review
  • meta-analysis

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