title: A composite likelihood method for bivariate meta-analysis in diagnostic systematic reviews.
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.
authors.affiliation: 1 Division of Biostatistics, The University of Texas Health Science Center at Houston, Houston, USA.
authors.forename: Yong
authors.initials: Y
authors.lastname: Chen
authors.affiliation: 1 Division of Biostatistics, The University of Texas Health Science Center at Houston, Houston, USA.
authors.forename: Yulun
authors.initials: Y
authors.lastname: Liu
authors.affiliation: 2 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, USA.
authors.forename: Jing
authors.initials: J
authors.lastname: Ning
authors.affiliation: 3 Department of Epidemiology and Preventive Medicine, University of Maryland at Baltimore, Baltimore, USA.
authors.forename: Lei
authors.initials: L
authors.lastname: Nie
authors.affiliation: 1 Division of Biostatistics, The University of Texas Health Science Center at Houston, Houston, USA.
authors.forename: Hongjian
authors.initials: H
authors.lastname: Zhu
authors.affiliation: 4 Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, USA.
authors.forename: Haitao
authors.initials: H
authors.lastname: Chu
mesh_terms: D000704:Analysis of Variance; D056808:Biostatistics / Q000379:methods; D003198:Computer Simulation; D003955:Diagnostic Tests, Routine / Q000706:statistics & numerical data*; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008545:Melanoma / Q000000981:diagnostic imaging / Q000556:secondary; D015201:Meta-Analysis as Topic; D016017:Odds Ratio; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000000981:diagnostic imaging
keywords: Bivariate generalized linear mixed effects model; composite likelihood; diagnostic accuracy; diagnostic review; meta-analysis