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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.
Health stats visible at Monitor.