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Deriving Clinically Interpretable Cutoffs for Disease Biomarkers

An R package for biomarker evaluation that introduces a two‑stage cutoff framework. Instead of a single threshold, it defines both a rule‑out cutoff (cL) and a rule‑in cutoff (cH), creating three groups: non‑diseased, diseased, and an intermediate gray zone. This approach improves diagnostic interpretability and supports clinical decision‑making.

✨ Features

The TwoCutoff package supports reproducible diagnostic pipelines by integrating:

• 📌 adjust_score() – Gaussian mixture + confounder‑adjusted XGBoost risk modeling
  
• 📊 derive_cutoffs_percentile() – percentile‑based cutoff derivation
  
• 📊 derive_cutoffs_sensspec() – ROC‑based cutoff derivation
  
• 📈 evaluate_performance() – sensitivity, specificity, PPV, NPV metrics
  
• 🔍 compare_performance() – side‑by‑side comparison of cutoff methods
  
• 📉 dca_analysis() – decision curve analysis for clinical utility
  
• 🎨 plot_two_cutoff() – visualization of cutoffs and confusion matrix

🛠 Installation

# Install from CRAN (when available) 
install.packages("TwoCutoff")

# Or install the development version from GitHub:
install.packages("remotes")
remotes::install_github("kumarbhrigu/TwoCutoff")

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.