Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Jingyi Guo, Andrea Riebler
Title: meta4diag: Bayesian Bivariate Meta-Analysis of Diagnostic Test Studies for Routine Practice
Abstract: This paper introduces the R package meta4diag for implementing Bayesian bivariate meta-analyses of diagnostic test studies. Our package meta4diag is a purpose-built front end of the R package INLA. While INLA offers full Bayesian inference for the large set of latent Gaussian models using integrated nested Laplace approximations, meta4diag extracts the features needed for bivariate meta-analysis and presents them in an intuitive way. It allows the user a straightforward model specification and offers user-specific prior distributions. Further, the newly proposed penalized complexity prior framework is supported, which builds on prior intuitions about the behaviors of the variance and correlation parameters. Accurate posterior marginal distributions for sensitivity and specificity as well as all hyperparameters, and covariates are directly obtained without Markov chain Monte Carlo sampling. Further, univariate estimates of interest, such as odds ratios, as well as the summary receiver operating characteristic (SROC) curve and other common graphics are directly available for interpretation. An interactive graphical user interface provides the user with the full functionality of the package without requiring any R programming. The package is available from the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=meta4diag/ and its usage will be illustrated using three real data examples.

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Paper: meta4diag: Bayesian Bivariate Meta-Analysis of Diagnostic Test Studies for Routine Practice     Download PDF (Downloads: 755)
Supplements:
meta4diag_2.0.7.tar.gz: R source package Download (Downloads: 91; 1MB)
v83i01.R: R replication code Download (Downloads: 104; 5KB)

DOI: 10.18637/jss.v083.i01

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.