top of page

                                                                             Automated Survival Analysis Platform

                                                                                             

 Overview

 

A web-based Shiny application for comprehensive survival analysis with enhanced data detection, reference level control, and automated reporting capabilities designed to support researchers and clinicians in conducting rigorous survival analyses.

 

Key Features

 

  • Intelligent Data Detection: Automatically recognizes various column naming patterns including cardiovascular-specific terms (time: survival_time, os_months, cardiac_months, cv_time; status: death, event, cv_event, cardiac_death)

  • Smart Data Conversion: Handles multiple formats (Yes/No, Dead/Alive, 0/1, Event/Censored) with automatic format recognition and conversion

  • Reference Level Control: Set meaningful baseline categories for hazard ratio interpretation with clinical relevance

  • Complete Analysis Suite: Kaplan-Meier plots, median survival times with confidence intervals, log-rank tests, pairwise comparisons, Cox regression, proportional hazards testing

  • Professional Reporting: Export comprehensive Word documents with formatted tables, statistical interpretations, and methodology notes

 

Supported Data Formats

 

  • Time Columns: time, survival_time, os_months, duration, followup_time, cardiac_months, cv_time, heart_disease_years, mi_time

  • Status Columns: status, event, death, censored, cardiac_death, cv_event, outcome, vital_status, cardiac_event, cardiovascular_death

  • File Format: CSV files with automatic encoding detection (UTF-8 recommended)

 

User Guide

Live Application Access Direct Link:

 

https://kadiroo-jayaraman.shinyapps.io/survival-analysis-platform/

​

The application is ready to use immediately - no installation or setup required.

 

Step-by-Step Instructions

 

  1. Upload Data: Select CSV file with survival time and event status columns

  2. Automatic Detection: App identifies time/status columns and converts formats (with user notifications)

  3. Variable Selection: Choose primary variable for Kaplan-Meier plots and select adjustment variables for Cox regression

  4. Reference Levels: Set clinically meaningful reference categories for categorical variables

  5. Analysis Review: Examine survival curves, median survival times, Cox regression results, and proportional hazards tests

  6. Export Reports: Generate comprehensive Word documents with all analyses, tables, and interpretations

 

Analysis Tabs

 

  • Kaplan-Meier Plot: Interactive survival curves with risk tables and p-values

  • Survival Summary: Median survival times, log-rank tests, and pairwise comparisons

  • Cox Regression: Multivariable analysis with hazard ratios and confidence intervals

  • Proportional Hazards Test: Assumption testing with Schoenfeld residuals and diagnostic plots

  • Data View: Review processed dataset with applied transformations

 

Example Use Cases

 

  • Clinical trial efficacy analysis

  • Cardiovascular outcomes research

  • Cancer survival studies

  • Epidemiological cohort analysis

  • Quality improvement projects

  • Time-to-event analysis in any medical specialty

 

Technical Specifications

 

Performance Notes

 

  • Optimized for datasets up to 10,000 patients

  • Cox regression supports up to 50 covariates

  • Word export handles reports up to 20 pages efficiently

  • Real-time data processing with immediate feedback

 

Security Considerations

 

  • No patient identifiers should be uploaded (app automatically skips ID-like columns)

  • Data processing occurs in-memory only (no persistent storage)

  • No data retention after session ends

  • Consider institutional data policies for sensitive research data

 

Technical Support

 

  • Detailed error messages provided in app notifications

  • Verify CSV file encoding (UTF-8 recommended)

  • Ensure categorical variables have reasonable number of levels (<20 recommended)

  • Check column naming patterns match supported formats

  • Contact: kadirooj@gmail.com for technical issues

 

Compliance and Limitations

 

  • Research Use: Designed for research purposes; clinical decisions require biostatistician consultation

  • Statistical Validity: Results include appropriate statistical warnings and interpretation guidelines

  • Model Assumptions: Proportional hazards assumption testing included for Cox regression validity

  • Data Quality: Users responsible for data cleaning and clinical interpretation

  • Professional Oversight: Complex analyses should be reviewed by qualified statisticians

 

​

bottom of page