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Visualizing the quantile survival time difference curve.

Harald Heinzl1, Martina Mittlboeck

  • 1Section for Clinical Biometrics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.

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Summary
This summary is machine-generated.

This study introduces a method to compare patient survival between therapies using quantile differences, visualized with Kaplan-Meier curves. A SAS macro, %kmdiff, facilitates this analysis, offering insights into survival time differences.

Keywords:
Kaplan-Meier curvesSAS macrobootstrap samplesconfidence bandsexploratory data analysis

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Clinical Research Methodology

Background:

  • Comparing patient survival between therapies is crucial in clinical research.
  • Existing methods may not fully capture survival differences across the entire survival spectrum.

Purpose of the Study:

  • To present a method for comparing two survival functions using quantile differences.
  • To introduce a user-friendly SAS macro (%kmdiff) for implementing this graphical approach.
  • To illustrate the application and discuss the advantages/limitations of quantile-based survival comparison.

Main Methods:

  • Estimating the difference between pth quantiles of two survival functions.
  • Utilizing Kaplan-Meier curves to visualize quantile survival time differences.
  • Employing bootstrap methods for variability assessment and confidence band generation.

Main Results:

  • The quantile survival time difference curve provides a comprehensive comparison of survival functions.
  • The %kmdiff macro simplifies the application of this exploratory graphical method.
  • The approach was successfully exemplified using breast cancer patient data.

Conclusions:

  • Quantile differences offer a flexible way to compare survival across different time points.
  • The %kmdiff macro enhances the accessibility of this survival analysis technique.
  • This method aids in a nuanced understanding of therapeutic effects on patient survival.