bonsaiforest2

The goal of bonsaiforest2 is to simplify fitting and interpreting Bayesian models for subgroup analysis in clinical trials. It leverages the power of the brms package to:

• Distinguish between prognostic (baseline predictors) and predictive (treatment effect modifiers) factors.
• Apply differential shrinkage priors (like Horseshoe or R2D2) to explore subgroup effects robustly.
• Calculate interpretable marginal treatment effects using a counterfactual approach.
• Generate publication-ready forest plots.

Installation

You can install the development version of bonsaiforest2 from its GitLab repository:

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

Example

This is a basic example showing the main workflow: fitting a simple continuous outcome model with shrinkage on a subgroup interaction.

library(bonsaiforest2)
library(brms) # Needed for backend

# 1. Minimal data setup
set.seed(123)
n <- 50 # Small n for README example
sim_data_readme <- data.frame(
  outcome = rnorm(n),
  trt = factor(sample(0:1, n, replace = TRUE)),
  age = rnorm(n, 50, 10),
  region = factor(sample(c("A", "B"), n, replace = TRUE))
)

# 2. Fit a simple model (use very few iterations for speed!)
# Shrink the treatment:region interaction
fit <- run_brms_analysis(
  data = sim_data_readme,
  response_formula_str = "outcome ~ trt",
  response_type = "continuous",
  unshrunk_prognostic_formula_str = "~ age",     # Adjust for age
  shrunk_predictive_formula_str = "~ trt:region", # Explore region interaction
  chains = 1, iter = 50, warmup = 25, refresh = 0, # Keep it FAST
  backend = "cmdstanr" # Optional: Specify backend if needed
)

# 3. Summarize marginal effects (will auto-detect 'region')
effect_summary <- summary_subgroup_effects(
  brms_fit = fit,
  original_data = sim_data_readme,
  trt_var = "trt",
  response_type = "continuous"
)

# 4. Plot the results
plot(effect_summary) # Display plot

Summarized Effects:

#> # A tibble: 3 × 4
#>   Subgroup  Median CI_Lower CI_Upper
#>   <chr>      <dbl>    <dbl>    <dbl>
#> 1 Overall    0.346  -0.0276    0.837
#> 2 region: A  0.260  -0.569     0.776
#> 3 region: B  0.457   0.0312    1.45