The bonsaiforest2 package is used for Bayesian shrinkage estimation of
subgroup treatment effects in randomized clinical trials. It supports
both One-Variable-at-a-Time (OVAT) and Global modeling approaches for
estimating treatment-by-subgroup interactions, with built-in support for
continuous, binary, time-to-event (Cox), and count outcomes. The package
implements state-of-the-art shrinkage priors including Regularized
Horseshoe and R2D2, combined with standardization (G-computation) to
provide interpretable marginal treatment effects. By leveraging brms
and Stan, bonsaiforest2 provides a practical tool for obtaining more
stable and reliable subgroup effect estimates in exploratory analyses.
UPDATE TO USUAL INSTALLATION You can install the development version
of bonsaiforest2 from its GitLab repository:
# install.packages("remotes")
remotes::install_github("openpharma/bonsaiforest2")This example demonstrates the usage of bonsaiforest2 for subgroup
treatment effect estimation across multiple overlapping subgroups (Age,
Region, and Biomarker) using a Global Modeling approach with a
Regularized Horseshoe prior.
library(bonsaiforest2)
# 1. Simulate trial data
set.seed(42)
n <- 200
trial_data <- data.frame(
outcome = rnorm(n),
trt = factor(sample(c("Control", "Active"), n, replace = TRUE)),
age_cat = factor(sample(c("<65", ">=65"), n, replace = TRUE)),
region = factor(sample(c("US", "EU", "Asia"), n, replace = TRUE)),
biomarker = factor(sample(c("Pos", "Neg"), n, replace = TRUE))
)
# 2. Fit a Global Model with default priors
fit <- run_brms_analysis(
data = trial_data,
response_type = "continuous",
response_formula = outcome ~ trt,
unshrunk_terms_formula = ~ age_cat + region + biomarker,
shrunk_predictive_formula = ~ 0 + trt:age_cat + trt:region + trt:biomarker,
sigma_ref = 3,
chains = 2, iter = 1000, warmup = 500 #
)
# 3. Derive Marginal Treatment Effects
subgroup_effects <- summary_subgroup_effects(
brms_fit = fit
)# Print summary
print(subgroup_effects)
#> $estimates
#> # A tibble: 7 × 4
#> Subgroup Median CI_Lower CI_Upper
#> <chr> <dbl> <dbl> <dbl>
#> 1 age_cat: <65 0.128 -0.241 0.516
#> 2 age_cat: >=65 -0.184 -0.566 0.219
#> 3 region: Asia -0.109 -0.544 0.366
#> 4 region: EU -0.349 -0.828 0.101
#> 5 region: US 0.387 -0.0988 0.918
#> 6 biomarker: Neg -0.0508 -0.426 0.258
#> 7 biomarker: Pos -0.00256 -0.326 0.346
#>
#> $response_type
#> [1] "continuous"
#>
#> $ci_level
#> [1] 0.95
#>
#> $trt_var
#> [1] "trt"
#>
#> attr(,"class")
#> [1] "subgroup_summary"
# 4. Visualize Results
plot(subgroup_effects)
#> Preparing data for plotting...
#> Generating plot...
#> Done.