• MR-BRT is a meta-regression tool created by Sasha and Peng, refactored for GBD 2020
• The R package mirrors the syntax of the Python package; use help() for R documentation and py_help() for Python documentation
• Functions: MRData formats the data, MRBRT and MRBeRT are for running models, and create_draws is for making predictions
• Find full code examples at rpubs.com/rsoren/587723, and find these slides at rpubs.com/rsoren/587724

Think of MR-BRT as a combination of…

• Linear regression like lm() – not glm()
• Mixed models with lme4
• Meta-analysis with metafor
• Splines with mgcv
• Bayesian priors like INLA

Also includes:

• Z-covariates, for predicting between-study variation
• Optimized knot placement for splines, a.k.a. “ensemble knots”
• Outlier trimming, Lasso variable selection
• Comparisons of exposure ranges, a.k.a. “the ratio model”

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summary(cars)

##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00