jsurvival

Abstract

jsurvival is a comprehensive survival analysis module for jamovi that bridges the gap between advanced statistical methods and clinical research accessibility. As part of the ClinicoPath statistical suite, it transforms complex survival analyses into intuitive, publication-ready outputs with natural language interpretations. The module implements state-of-the-art survival analysis techniques including Kaplan-Meier estimation, Cox proportional hazards regression, and time-dependent analyses, while maintaining a user-friendly interface designed for medical researchers. By automating person-time calculations, providing automated statistical summaries in plain language, and generating high-quality visualizations, jsurvival enables clinicians and researchers to conduct sophisticated survival analyses without extensive programming knowledge, ultimately accelerating the translation of clinical data into actionable insights.

🎯 Key Features

Core Survival Analysis Capabilities

• Kaplan-Meier Analysis: Generate survival curves with confidence intervals, risk tables, and median survival times
• Cox Proportional Hazards Models: Both univariate and multivariable regression with hazard ratios and forest plots
• Person-Time Calculations: Automated computation of person-years at risk with incidence rate calculations
• Cut-point Analysis: Optimal threshold determination for continuous biomarkers using multiple methods
• Time-Dependent Analyses: Support for time-varying covariates and landmark analysis
• Competing Risks: Handle multiple event types with cause-specific hazard functions

Clinical Research Features

• Natural Language Summaries: Automated generation of plain-English interpretations of results
• Clinical Trial Metrics: 1-, 3-, and 5-year survival rates with confidence intervals
• Stage Migration Analysis: Evaluate the Will Rogers phenomenon in cancer staging with advanced TNM staging validation
• Treatment Pathway Visualization: Alluvial plots for treatment sequences over time
• Subgroup Forest Plots: Systematic evaluation of treatment effects across patient subgroups
• Educational Explanations: Built-in HTML explanations for key analysis concepts and methods

Advanced Statistical Methods

• Restricted Mean Survival Time (RMST): Alternative to median survival for skewed distributions
• Time-Dependent ROC Curves: Evaluate biomarker performance over time
• LASSO-Cox Regression: Variable selection for high-dimensional survival data
• Integrated Discrimination Improvement (IDI): Compare predictive models
• Schoenfeld Residual Diagnostics: Test proportional hazards assumptions

User Experience Enhancements

• Intuitive GUI: Point-and-click interface within jamovi, no coding required
• Clinical Analysis Presets: Pre-configured settings for common study types (overall survival, disease-free survival, treatment effectiveness, post-surgical outcomes)
• Guided Setup Mode: Step-by-step guidance for users new to survival analysis
• Smart Defaults: Evidence-based default settings for common analyses
• Educational Tooltips: Context-sensitive help explaining statistical concepts
• Export Options: Publication-ready tables and figures in multiple formats
• Reproducible Reports: Generate complete analysis reports with code

📊 Available Analysis Modules

Module	Description	Key Features
Single Arm Survival	Analyze survival in a single cohort	Clinical presets, guided mode, overall survival rates, median survival, person-time calculations
Survival Analysis	Compare survival between groups	Educational explanations, log-rank test, Cox regression, stratified analysis
Continuous Survival	Analyze continuous predictors	Educational explanations, optimal cut-point detection, tertile/quartile analysis
Multivariable Survival	Adjust for multiple factors	Educational explanations, stepwise selection, interaction terms, adjusted curves
Odds Ratio Analysis	Binary outcome analysis	Educational explanations, 2x2 tables, forest plots, Mantel-Haenszel methods
Stage Migration	Will Rogers phenomenon	Advanced TNM staging, stage-specific survival, migration matrices, trend analysis

🚀 Installation

In jamovi (Recommended)

1. Open jamovi
2. Click the + button → jamovi library
3. Search for "ClinicoPath" or "jsurvival"
4. Click Install

As R Package

# Install from GitHub
devtools::install_github("sbalci/jsurvival")

# Load the package
library(jsurvival)

System Requirements

• jamovi ≥ 1.8.1
• R ≥ 4.1.0
• Dependencies: survival, survminer, finalfit, ggplot2, dplyr

📖 Documentation

• Package Documentation: https://www.serdarbalci.com/jsurvival/
• ClinicoPath Suite: https://sbalci.github.io/ClinicoPathJamoviModule/
• Tutorials: Available in the vignettes/ directory
• Example Data: Included datasets for learning and testing

💡 Quick Example

In jamovi:

1. Load your survival data
2. Navigate to Survival → ClinicoPath Survival → Survival Analysis
3. Set variables:
   • Time Elapsed: Time to event variable
   • Outcome: Event indicator (0/1)
   • Explanatory: Grouping variable
4. Click Run

In R:

# Load example data
data("melanoma", package = "jsurvival")

# Run survival analysis
result <- jsurvival::survival(
    data = melanoma,
    elapsedtime = "time",
    outcome = "status", 
    explanatory = "sex"
)

# View results
result$run()

📝 Citation

If you use jsurvival 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

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

📄 License

This project is licensed under the GPL (≥ 2) License - see the LICENSE.md file for details.

💬 Support

• Bug Reports: GitHub Issues
• Questions: Discussions
• Email: serdarbalci@serdarbalci.com
• ORCID: 0000-0002-7852-3851

🙏 Acknowledgments

This project builds upon the excellent work of the R survival analysis community, particularly the authors of the survival, survminer, and finalfit packages. Special thanks to the jamovi team for creating an accessible statistical platform.

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Part of the ClinicoPath suite of statistical modules for biomedical research.