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CASAS: Cancer Survival Analysis Suite, a web based application.

Manali Rupji1, Xinyan Zhang1, Jeanne Kowalski1,2

  • 1Winship Cancer Institute of Emory University, Atlanta, GA, 30322, USA.

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CASAS is an R-based tool for interactive survival analysis, offering quantile, landmark, and competing risks analyses. It simplifies complex survival data visualization and interpretation for researchers.

Keywords:
competing risklandmarkquantile survivalsurvival

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Area of Science:

  • Biostatistics
  • Bioinformatics
  • Computational Biology

Background:

  • Survival analysis is crucial in medical research for understanding time-to-event data.
  • Existing tools may lack comprehensive features or user-friendly interfaces for advanced survival analyses.
  • Interactive visualization and simultaneous analysis of multiple variables are often desired.

Purpose of the Study:

  • To introduce CASAS, a novel R-based tool for interactive survival analysis.
  • To provide a web-based platform for performing standard, quantile, landmark, and competing risks survival analyses.
  • To facilitate intuitive visualization and interpretation of survival analysis results.

Main Methods:

  • Development of CASAS using the R programming language and Shiny framework.
  • Implementation of interactive Kaplan-Meier and cumulative incidence plots for data visualization.
  • Integration of univariate analysis for multiple user-specified variables with results presented in a summary table.

Main Results:

  • CASAS offers a comprehensive suite of survival analysis methods in an accessible web interface.
  • Interactive plots and summary tables enhance the understanding of survival data.
  • The tool supports simultaneous analysis of multiple variables and generates summary grids for quantile survival analyses across cancer types.

Conclusions:

  • CASAS provides an efficient and interactive solution for diverse survival analyses.
  • The tool democratizes advanced survival analysis techniques for a wider research audience.
  • CASAS is available as an R package and via a web portal for ease of use and accessibility.