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NBvarsel

Variable selection for binary outcome prediction models using cross-validated Net Benefit as the optimization criterion.

Overview

NBvarsel evaluates predictor subsets by their contribution to clinical utility (Net Benefit), rather than purely statistical performance metrics like p-values. It supports:

• Exhaustive and groupwise (backward elimination) search strategies
• Predictor costs (per-variable or grouped)
• Restricted cubic splines for continuous predictors
• Interaction terms
• Permutation importance scores
• Parallel computation

Installation

You can install the development version of NBvarsel from GitHub:

# install.packages("pak")
pak::pak("LasaiBarrenada/NB_varsel")

Alternatively, install from a source tarball (.tar.gz):

install.packages("NBvarsel_0.1.0.tar.gz", repos = NULL, type = "source")

Quick Start

library(NBvarsel)

# Simulate data
set.seed(42)
n <- 500
df <- data.frame(
  X1 = rnorm(n), X2 = rbinom(n, 1, 0.7),
  X3 = rnorm(n), X4 = rbinom(n, 1, 0.5)
)
df$Y <- rbinom(n, 1, plogis(2 * df$X1 + 1.5 * df$X2 + 0.2 * df$X3))

# Define predictor costs
harms <- c(X1 = 0.1, X2 = 0.05, X3 = 0.1, X4 = 0.0001)

# Run exhaustive variable selection
result <- nb_varsel(
  data = df,
  outcome_var = "Y",
  costs = harms,
  mode = "exhaustive",
  splines = FALSE,
  permutation = TRUE,
  allow_parallel = FALSE
)

# Best model (highest cost-adjusted Net Benefit)
result$best_model_stats

# All evaluated models, sorted by Avg_Adj_Net_Benefit
result$all_models

Visualisation

# Two-panel plot: metric overview + predictor inclusion heatmap
all_subset_plot(result$all_models)

# Customise colors
all_subset_plot(
  result$all_models,
  highlight_color = "steelblue",
  tile_color = "#E64B35"
)

# Permutation importance bar chart
vif_results <- VIF_plot(result$all_models, color = "darkgreen")
vif_results$plot
head(vif_results$data)

Documentation

Full documentation and vignettes are available at https://lasaibarrenada.github.io/NB_varsel/.

Functions

References

• Barreñada, L., Vickers, A. J., Steyerberg, E. W., Timmerman, D., Wynants, L., & Van Calster, B. (2026). Cost-based variable selection to improve clinical utility of prediction models. In preparation.

• Vickers, A. J., & Elkin, E. B. (2006). Decision curve analysis: a novel method for evaluating prediction models. Medical Decision Making, 26(6), 565–574. https://doi.org/10.1177/0272989X06295361

• Van Calster, B., Wynants, L., Verbeek, J. F. M., Verbakel, J. Y., Christodoulou, E., Vickers, A. J., Roobol, M. J., & Steyerberg, E. W. (2018). Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators. European Urology, 74(6), 796–804. https://doi.org/10.1016/j.eururo.2018.08.038

• Vickers, A. J., Van Calster, B., & Steyerberg, E. W. (2019). A simple, step-by-step guide to interpreting decision curve analysis. Diagnostic and Prognostic Research, 3, 18. https://doi.org/10.1186/s41512-019-0064-7

• Baker, S. G., Van Calster, B., & Steyerberg, E. W. (2012). Evaluating a New Marker for Risk Prediction Using the Test Tradeoff: An Update. The International Journal of Biostatistics, 8, 1–37. https://doi.org/10.1515/1557-4679.1395

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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