Specialized Oncological and Pathological Research Tools for jamovi

OncoPath is a specialized jamovi module designed specifically for oncological and pathological research. It provides comprehensive patient follow-up visualization tools that are essential for clinical research, treatment response analysis, and patient timeline tracking.

• Patient Timeline Visualization: Comprehensive swimmer plots using enhanced ggswim package integration
• Multi-dimensional Data Support: Clinical events, milestones, treatment responses, and adverse events
• Enhanced Data Validation: Robust input validation with comprehensive error handling
• Flexible Timeline Display: Customizable patient journey visualization with event overlays
• Clinical Research Integration: Designed specifically for oncological clinical trial reporting

• Treatment Response Visualization: Comprehensive waterfall and spider plots for tumor response analysis
• RECIST Criteria Support: Built-in Response Evaluation Criteria In Solid Tumors (RECIST) guidelines
• Dual Data Input: Supports both raw tumor measurements and pre-calculated percentage changes
• Clinical Metrics: Automated calculation of ORR (Overall Response Rate), DCR (Disease Control Rate), and person-time metrics
• Publication Ready: Professional visualization suitable for clinical publications and presentations

• Immunohistochemistry Analysis: Statistical analysis of IHC marker heterogeneity
• Multi-marker Support: Comprehensive evaluation of multiple biomarkers
• Statistical Validation: Robust statistical methods for heterogeneity assessment
• Pathology Research: Specialized tools for immunohistochemical studies

• Bivariate Meta-Analysis: Advanced bivariate random-effects modeling using the Reitsma method
• HSROC Analysis: Hierarchical Summary ROC curve analysis for diagnostic accuracy
• Meta-Regression: Covariate analysis to explore heterogeneity sources
• Publication Bias Assessment: Comprehensive bias detection and visualization
• Forest and SROC Plots: Publication-ready visualizations for diagnostic test accuracy
• AI Algorithm Validation: Designed for validating AI/ML diagnostic algorithms in pathology
• Biomarker Studies: Comprehensive synthesis of diagnostic biomarker accuracy studies

• jamovi version 2.7.2 or higher

1. Open jamovi
2. Click on the "Modules" (⊞) button in the top-right
3. Select "jamovi library"
4. Search for "OncoPath"
5. Click "Install"

1. Download the latest .jmo file from releases
2. In jamovi, click "Modules" (⊞) → "Sideload"
3. Select the downloaded .jmo file

# Install from GitHub
remotes::install_github("sbalci/OncoPath")

1. Load your patient timeline data with columns for:

   • Patient ID
   • Start time
   • End time
   • Events (optional)
   • Response data (optional)

2. Navigate to OncoPath → Patient Follow-Up Plots → Swimmer Plot

3. Configure your variables and customize the visualization

1. Prepare your treatment response data with:

   • Patient ID
   • Response variable (percentage change or raw measurements)
   • Time points (for longitudinal analysis)
   • Group variables (optional)

2. Navigate to OncoPath → Patient Follow-Up Plots → Treatment Response Analysis

3. Select RECIST criteria options and customize your analysis

• Website: https://www.serdarbalci.com/OncoPath/
• Swimmer Plot Guide: https://www.serdarbalci.com/OncoPath/articles/swimmerplot_documentation.html
• Waterfall Plot Guide: https://www.serdarbalci.com/OncoPath/articles/waterfall_documentation.html

OncoPath includes sample datasets to help you get started:

• Swimmer Plot Analysis: swimmerplot_sample.omv
• Waterfall Plot: waterfall_percentage_basic.omv
• Waterfall and Spider Plot: waterfall_raw_longitudinal.omv

• R (≥ 4.1.0)
• jmvcore (≥ 0.8.5)
• ggplot2
• dplyr
• rlang

• ggswim: Enhanced swimmer plot functionality
• mada: Meta-analysis of diagnostic accuracy studies
• metafor: Meta-regression and advanced meta-analysis methods
• pROC: ROC curve analysis for diagnostic tests
• survival & survminer: Survival analysis and visualization
• lubridate: Date/time handling
• RColorBrewer: Professional color schemes
• gridExtra: Advanced plot layouts
• boot, dcurves, Hmisc, rms, timeROC: Advanced statistical methods

• Clinical Trial Reporting: Patient timelines and treatment responses
• Longitudinal Studies: Disease progression and treatment effects over time
• Oncology Research: Tumor response evaluation following RECIST guidelines
• Diagnostic Accuracy Studies: Meta-analysis of biomarker and diagnostic test performance
• AI Algorithm Validation: Systematic review and meta-analysis of AI-based diagnostic tools

• Biomarker Validation: Comprehensive meta-analysis of diagnostic biomarkers
• IHC Studies: Statistical analysis of immunohistochemistry heterogeneity
• Systematic Reviews: Synthesis of diagnostic test accuracy across multiple studies
• Method Comparison: Evaluation of different diagnostic methods and techniques

• Manuscript Figures: Publication-ready visualizations with professional styling
• Conference Presentations: Clear, informative plots for academic presentations
• Regulatory Submissions: Standardized reporting formats for regulatory agencies
• Meta-Analysis Reports: Comprehensive forest plots, SROC curves, and funnel plots

We welcome contributions! Please see our Contributing Guidelines for details.

• Additional visualization options
• Enhanced RECIST criteria support
• New clinical event types
• Documentation improvements
• Bug reports and feature requests

• Issues: Report bugs or request features on GitHub Issues
• Discussions: Join the conversation in GitHub Discussions
• Email: Contact the maintainer at serdarbalci@serdarbalci.com

If you use OncoPath in your research, please cite the main ClinicoPath project:

Serdar Balci (2025). ClinicoPath jamovi Module. doi:10.5281/zenodo.3997188
[R package]. Retrieved from https://github.com/sbalci/ClinicoPathJamoviModule

GPL (>= 2) - see LICENSE file for details.

OncoPath is part of the ClinicoPath ecosystem:

• ClinicoPathDescriptives: Descriptive statistics and data quality tools
• jsurvival: Comprehensive survival analysis
• meddecide: Medical decision analysis and ROC curves
• jjstatsplot: Statistical visualization with ggstatsplot integration

Developed by Serdar Balci