Bayesian meta-analysis of diagnostic test data based on a scale mixtures bivariate random-effects model. This package was developed with the aim of simplifying the use of meta-analysis models that up to now have demanded great statistical expertise in Bayesian meta-analysis. The package implements a series of innovative statistical techniques including: the BSROC (Bayesian Summary ROC) curve, the BAUC (Bayesian AUC), predictive surfaces, the use of prior distributions that avoid boundary estimation problems of component of variance and correlation parameters, analysis of conflict of evidence and robust estimation of model parameters. In addition, the package comes with several published examples of meta-analysis that can be used for illustration or further research in this area.
|License:||GPL (>= 2)|
PD Dr. Pablo Emilio Verde email@example.com
Verde P. E. (2010). Meta-analysis of diagnostic test data: A bivariate Bayesian modeling approach. Statistics in Medicine. 29(30):3088-102. doi: 10.1002/sim.4055.
Verde P. E. (2018). bamdit: An R Package for Bayesian Meta-Analysis of Diagnostic Test Data. Journal of Statisticsl Software. Volume 86, issue 10, pages 1–32.
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