Introduction to Bivariate Meta Analysis

Bivariate meta-analysis was performed using the mada procedure in R.

In this paper, we establish the random-effect model of bivariate meta-analysis based on the difference mean.

Random-effects bivariate meta-analysis was conducted to estimate the summary sensitivity, specificity, likelihood ratios, and area under receiver operating characteristic curve.

A bivariate meta-analysis was used to estimate summary sensitivities and specificities of MRI for avulsion.

We used bivariate meta-analysis to produce summary estimates of diagnostic accuracy at prespecified positivity thresholds of 80 and 120 pg/ml.

Sensitivities and specificities were pooled by bivariate meta-analysis.

The bivariate meta-analysis model was employed to plot the summary receiver operator characteristic (SROC) curve using Stata 14.

We performed bivariate meta-analysis to obtain the pooled sensitivity, specificity, positive, negative likelihood ratio and diagnostic odds ratio for each of the GDS forms.

We aimed to perform a systematic review and a bivariate meta-analysis on the diagnostic role of 18F-FDG PET/CT in patients with SI.

Summary estimates of sensitivity according to the CRC stage and study setting (screening cohorts, symptomatic/diagnostic cohorts, and case-control studies) were derived from bivariate meta-analysis.

A bivariate meta-analysis was used to pool screening tool-specific accuracy estimates (sensitivity and specificity).

The diagnostic and prognostic parameters were respectively synthesized using the bivariate meta-analysis model and multivariate Cox hazard regression analysis based on Stata 12.

Bivariate meta-analysis is known as a promising approach for jointly combining two outcomes whilst incorporating correlations between outcomes.

All review stages were conducted independently by 2 reviewers, and where possible data were pooled using bivariate meta-analysis.

The diagnostic accuracy of a lateral cephalogram for adenoid hypertrophy and posterior upper airway obstruction was calculated using a bivariate meta-analysis model.

We also compared the diagnostic odds ratio (DOR), heterogeneity in DOR, and false positive rate (FPR) for each signature using bivariate meta-analysis.

A bivariate meta-analysis model was used to calculate the pooled sensitivity, specificity, positive/negative likelihood ratios and diagnostic odds ratio.

This paper introduces the unknown equal variance model of bivariate Meta-analysis based on the difference mean.

Bayesian bivariate meta-analysis was performed, presenting 95% confidence intervals (CI).

We performed a bivariate meta-analysis of sensitivity and specificity using a generalized linear mixed model.

We used bivariate meta-analysis to calculate summary estimates of test accuracy.

Bivariate meta-analysis using a random effects model was used to calculate summarized pooled estimates of sensitivity, specificity, and diagnostic odds ratios, using second-look surgery as the gold standard comparison.

ABSTRACT We propose a bivariate Farlie–Gumbel–Morgenstern (FGM) copula model for bivariate meta-analysis, and develop a maximum likelihood estimator for the common mean vector.

Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), and measures of accuracy calculated using bivariate meta-analysis.