Overview | Workflow Overview | Adjusted Risk Model | Stage 1 — Finite Mixture Model (FMM) | Stage 2 — Confounder-Adjusted Risk Model | Function Usage | Percentile-Based Cutoff Derivation | Method Overview | Low-Risk Group and Rule-Out Cutoff | High-Risk Group and Rule-In Cutoff | Fallback Behavior for Small Datasets | Classification Rules | ROC-Based Cutoff Derivation | ROC Curve Construction | Selection of Rule-Out and Rule-In Thresholds | Rule-Out Threshold (T_L) | Rule-In Threshold (T_H) | Mapping Risk Thresholds Back to Biomarker Values | Performance Evaluation | Performance Metrics | Interpretation | Comparison of Cutoff Methods | Visualization of Two-Cutoff Classification | Classification Regions | Plot Structure | Confusion Matrix Visualization | Multi-Panel Visualization | Single-Method Visualization | Comparison of Multiple Cutoff Methods | Decision Curve Analysis | Net Benefit | Strategies Compared | Decision Curve Analysis Example
Package: TwoCutoff 0.1.0
TwoCutoff: Deriving Clinically Interpretable Cutoffs for Disease Biomarkers
Provides a reproducible pipeline for deriving two clinically meaningful cutoffs for disease biomarkers using a unified two-stage framework. The package integrates finite mixture modeling with risk prediction using biomarker plus clinical features, followed by decision curve analysis to evaluate clinical utility. Outputs include biomarker density plots, risk calibration curves, decision curves, and summary tables of diagnostic performance. Designed for researchers in bio-statistics, neurology, and data science, this package emphasizes reproducibility, transparency, and clear clinical relevance.
Authors:
TwoCutoff_0.1.0.tar.gz
TwoCutoff_0.1.0.zip(r-4.7)TwoCutoff_0.1.0.zip(r-4.6)TwoCutoff_0.1.0.zip(r-4.5)
TwoCutoff_0.1.0.tgz(r-4.6-any)TwoCutoff_0.1.0.tgz(r-4.5-any)
TwoCutoff_0.1.0.tar.gz(r-4.7-any)TwoCutoff_0.1.0.tar.gz(r-4.6-any)
TwoCutoff_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
TwoCutoff/json (API)
| # Install 'TwoCutoff' in R: |
| install.packages('TwoCutoff', repos = c('https://kumarbhrigu.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- alzheimer_data - Alzheimer’s Biomarker Dataset
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:46a709ed0e. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 240 | ||
| source / vignettes | OK | 326 | ||
| linux-release-x86_64 | OK | 230 | ||
| macos-release-arm64 | OK | 193 | ||
| macos-oldrel-arm64 | OK | 209 | ||
| windows-devel | OK | 142 | ||
| windows-release | OK | 143 | ||
| windows-oldrel | OK | 130 | ||
| wasm-release | OK | 197 |
Exports:adjust_scorecompare_performancedca_analysisderive_cutoffs_percentilederive_cutoffs_sensspecevaluate_performanceplot_two_cutoff
Dependencies:askpassbase64encbslibcachemcaretclasscliclockcodetoolscpp11crosstalkcurldata.tablediagramdigestdplyre1071evaluatefarverfastmapfontawesomeforeachfsfuturefuture.applygenericsggplot2globalsgluegowergridExtragtablehardhathighrhtmltoolshtmlwidgetshttripredisobanditeratorsjquerylibjsonlitekernlabKernSmoothknitrlabelinglaterlatticelavalazyevallifecyclelistenvlubridatemagrittrMASSMatrixmemoisemimemixtoolsModelMetricsnlmennetnumDerivopensslotelparallellypatchworkpillarpkgconfigplotlyplyrpROCprodlimprogressrpromisesproxypurrrR6rappdirsRColorBrewerRcpprecipesreshape2rlangrmarkdownrpartS7sassscalessegmentedshapesparsevctrsSQUAREMstringistringrsurvivalsystibbletidyrtidyselecttimechangetimeDatetinytextzdbutf8vctrsviridisLitewithrxfunxgboostyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Adjust biomarker score with confounder-adjusted risk model | adjust_score |
| Alzheimer’s Biomarker Dataset | alzheimer_data |
| Compare cutoff methods | compare_performance |
| Decision Curve Analysis (DCA) | dca_analysis |
| Derive percentile-based biomarker cutoffs using adjusted risk | derive_cutoffs_percentile |
| Derive ROC-based biomarker cutoffs using adjusted risk | derive_cutoffs_sensspec |
| Evaluate classification performance | evaluate_performance |
| Plot two-cutoff classification | plot_two_cutoff |
| Print method for derive_cutoffs_percentile objects | print.cutoff.percentile |
| Print method for derive_cutoffs_sensspec objects | print.cutoff.sensspec |
| Print Method for twocutoff.adjscore Objects | print.twocutoff.adjscore |
