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Software Application Profile: Bayesian estimation of inverse variance weighted and MR-Egger models for two-sample Mendelian randomization studies-mrbayes

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>28/02/2021
<mark>Journal</mark>International Journal of Epidemiology
Issue number1
Number of pages7
Pages (from-to)43-49
Publication StatusPublished
Early online date8/12/20
<mark>Original language</mark>English


Motivation: We present our package, mrbayes, for the open source software environment R. The package implements Bayesian estimation for inverse variance weighted (IVW) and MR-Egger models, including the radial MR-Egger model, for summary-level data in Mendelian randomization (MR) analyses. Implementation: We have implemented a choice of prior distributions for the model parameters, namely; weakly informative, non-informative, a joint prior for the MR-Egger model slope and intercept, and an informative prior (pseudo-horseshoe prior), or the user can specify their own prior distribution. General features: Users have the option of fitting the models using either JAGS or Stan software packages with similar prior distributions; the option for the user-defined prior distribution is only in our JAGS functions. We show how to use the package through an applied example investigating the causal effect of body mass index (BMI) on acute ischaemic stroke. Availability: The package is freely available, under the GNU General Public License v3.0, on GitHub [https://github.com/okezie94/mrbayes] or CRAN [https://CRAN.R-project.org/package=mrbayes].