The goal of MetaForest is to explore heterogeneity in meta-analytic data, identify important moderators, and explore the functional form of the relationship between moderators and effect size. To do so, MetaForest conducts a weighted random forest analysis, using random-effects or fixed-effects weights, as in classic meta-analysis, or uniform weights (unweighted random forest). Simulation studies have demonstrated that this technique has substantial power to detect relevant moderators, even in datasets as small as 20 cases (based on cross-validated R2). Using a variable importance plot, important moderators can be identified, and using partial prediction plots, the shape of the marginal relationship between moderators and effect size can be visualized. MetaForest can be readily integrated in classical meta-analytic approaches: If MetaForest is conducted as a primary analysis, classic meta-analysis can be used to quantify heterogeneity (in fact, MetaForest by default reports a random-effects meta-analysis on the raw data, and the residuals of the random forests analysis), or to provide a simplified representation of the linear effects of important predictors. Conversely, a theory-driven classical meta-analysis could be complemented by an exploratory MetaForest analysis, as a final check to ensure that important moderators have not been overlooked. We hope that this approach will be of use to researchers, and that the availability of user-friendly R functions will facilitate its adoption.
You can install metaforest from CRAN with:
Every user-facing function in the package is documented, and the documentation can be accessed by running ?function_name in the R console, e.g., ?graph, or by checking the project website
You can cite the method by referencing this open access book chapter:
Van Lissa, C. J. (2020). Small sample meta-analyses: Exploring heterogeneity using MetaForest. In R. Van De Schoot & M. Miočević (Eds.), Small Sample Size Solutions (Open Access): A Guide for Applied Researchers and Practitioners. CRC Press. https://doi.org/10.4324/9780429273872-16
The simulation study supporting the method is available in:
Van Lissa, C. J. (2018). MetaForest: Exploring heterogeneity in meta-analysis using random forests. PsyArxiv. https://doi.org/10.31234/osf.io/myg6s
If you have ideas, please get involved. You can contribute by opening an issue on GitHub, or sending a pull request with proposed features.
By participating in this project, you agree to abide by the Contributor Code of Conduct v2.0. Contributions to the package must adhere to the tidyverse style guide. When contributing code, please add tests for that contribution to the tests/testthat folder, and ensure that these tests pass in the GitHub Actions panel.
This example demonstrates how one might go about conducting a meta-analysis using MetaForest. For more information, check the package vignette.
#Load metaforest package
library(metaforest)
#Simulate a meta-analysis dataset with 20 studies, 1 relevant moderator, and 4 irrelevant moderators
set.seed(42)
data <- SimulateSMD()$training
#Conduct an unweighted MetaForest analysis, to estimate the residual tau2
mf.unif <- MetaForest(formula = yi ~ ., data = data,
whichweights = "unif", method = "DL", num.trees = 2000)
#Extract the result of this analysis and print them
results <- summary(mf.unif)
results
#> MetaForest results
#>
#> Type of analysis: MetaForest
#> Number of studies: 20
#> Number of moderators: 5
#> Number of trees in forest: 2000
#> Candidate variables per split: 2
#> Minimum terminal node size: 5
#> OOB prediction error (MSE): 0.1012
#> R squared (OOB): 0.2970
#>
#> Tests for Heterogeneity:
#> tau2 tau2_SE I^2 H^2 Q-test df Q_p
#> Raw effect sizes: 0.0553 0.0486 37.2642 1.5940 30.2857 19 0.0483
#> Residuals (after MetaForest): 0.0099 0.0334 9.6420 1.1067 21.0275 19 0.3353
#>
#>
#> Random intercept meta-analyses:
#> Intercept se ci.lb ci.ub p
#> Raw effect sizes: -0.2136 0.0875 -0.3851 -0.0421 0.0147
#> Residuals (after MetaForest): 0.0357 0.0720 -0.1053 0.1768 0.6197
#Conduct a weighted MetaForest analysis, using the residual tau2 from the
#unweighted analysis above
mf.random <- MetaForest(formula = yi ~ ., data = data,
whichweights = "random", method = "DL",
tau2 = results$rma[2,1],
num.trees = 2000)
#Print the result of this analysis
summary(mf.random)
#> MetaForest results
#>
#> Type of analysis: MetaForest
#> Number of studies: 20
#> Number of moderators: 5
#> Number of trees in forest: 2000
#> Candidate variables per split: 2
#> Minimum terminal node size: 5
#> OOB prediction error (MSE): 0.0945
#> R squared (OOB): 0.3438
#>
#> Tests for Heterogeneity:
#> tau2 tau2_SE I^2 H^2 Q-test df Q_p
#> Raw effect sizes: 0.0553 0.0486 37.2642 1.5940 30.2857 19 0.0483
#> Residuals (after MetaForest): 0.0031 0.0312 3.2094 1.0332 19.6300 19 0.4171
#>
#>
#> Random intercept meta-analyses:
#> Intercept se ci.lb ci.ub p
#> Raw effect sizes: -0.2136 0.0875 -0.3851 -0.0421 0.0147
#> Residuals (after MetaForest): 0.0298 0.0693 -0.1059 0.1656 0.6666