The Galaxy Plot: A New Visualization Tool for Bivariate Meta-Analysis Studies

Funnel plots have been widely used to detect small-study effects in the results of univariate meta-analyses. However, there is no existing visualization tool that is the counterpart of the funnel plot in the multivariate setting. We propose a new visualization method, the galaxy plot, which can simultaneously present the effect sizes of bivariate outcomes and their standard errors in a 2-dimensional space. We illustrate the use of the galaxy plot with 2 case studies, including a meta-analysis of hypertension trials with studies from 1979–1991 (Hypertension. 2005;45(5):907–913) and a meta-analysis of structured telephone support or noninvasive telemonitoring with studies from 1966–2015 (Heart. 2017;103(4):255–257). The galaxy plot is an intuitive visualization tool that can aid in interpreting results of multivariate meta-analysis. It preserves all of the information presented by separate funnel plots for each outcome while elucidating more complex features that may only be revealed by examining the joint distribution of the bivariate outcomes.

3128 Copula-Based Regression Models for Correlated Bivariate Binary Outcomes: Application to Ophthalmologic Data Structures

OBJECTIVES/SPECIFIC AIMS: To account for association between the pair of binary outcomes, we adopt the Clayton and Frank copulas to indirectly specify their joint distributions. METHODS/STUDY POPULATION: We propose a regression model for the joint modelling of correlated bivariate outcomes using copulas. RESULTS/ANTICIPATED RESULTS: develop full maximum likelihood inference.

Copas-like selection model to correct publication bias in systematic review of diagnostic test studies

The accuracy of a diagnostic test, which is often quantified by a pair of measures such as sensitivity and specificity, is critical for medical decision making. Separate studies of an investigational diagnostic test can be combined through meta-analysis; however, such an analysis can be threatened by publication bias. To the best of our knowledge, there is no existing method that accounts for publication bias in the meta-analysis of diagnostic tests involving bivariate outcomes. In this paper, we extend the Copas selection model from univariate outcomes to bivariate outcomes for the correction of publication bias when the probability of a study being published can depend on its sensitivity, specificity, and the associated standard errors. We develop an expectation-maximization algorithm for the maximum likelihood estimation under the proposed selection model. We investigate the finite sample performance of the proposed method through simulation studies and illustrate the method by assessing a meta-analysis of 17 published studies of a rapid diagnostic test for influenza.