bonsaiforest2

Overview

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.

Installation

UPDATE TO USUAL INSTALLATION You can install the development version of bonsaiforest2 from its GitLab repository:

# install.packages("remotes") 
remotes::install_github("openpharma/bonsaiforest2")

Example

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.