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.

• 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

• 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
• Treatment Pathway Visualization: Alluvial plots for treatment sequences over time
• Subgroup Forest Plots: Systematic evaluation of treatment effects across patient subgroups

• 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

• Intuitive GUI: Point-and-click interface within jamovi, no coding required
• 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

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

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

# Load the package
library(jsurvival)

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

• 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

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

# 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()

If you use jsurvival in your research, please cite:

Balci, S. (2025). jsurvival: Survival Module of ClinicoPath for jamovi. 
R package version 0.0.3.90. https://github.com/sbalci/jsurvival

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.

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

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

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.

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