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Inferring median survival differences in general factorial designs via permutation tests.

Marc Ditzhaus1, Dennis Dobler2, Markus Pauly1

  • 1Faculty of Statistics, TU Dortmund University, Dortmund, Germany.

Statistical Methods in Medical Research
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This study introduces a new method using median survival times to analyze factorial survival data, offering a clearer interpretation than hazard ratios, especially when hazards are non-proportional.

Keywords:
Censoringinteractionmedianresamplingsurvival

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Clinical Trial Design

Background:

  • Factorial survival designs with right-censored data are often analyzed using Cox regression and hazard ratios.
  • Interpreting hazard ratios can be challenging for clinicians when dealing with non-proportional hazards.

Purpose of the Study:

  • To propose an alternative statistical method for analyzing factorial survival data.
  • To provide a more interpretable alternative to hazard ratios, particularly in the presence of non-proportional hazards.

Main Methods:

  • Estimating treatment and interaction effects using median survival times.
  • Formulating null hypotheses in contrasts of population median survival times.
  • Developing permutation-based tests and confidence regions for hypothesis testing.

Main Results:

  • The proposed methods are asymptotically valid for factorial survival designs.
  • Extensive simulations demonstrate good type-1 error control and power.
  • The methods show wide applicability in survival data analysis.

Conclusions:

  • Median survival time analysis offers a practical and interpretable alternative to hazard ratios in factorial survival designs.
  • The new permutation-based methods are statistically sound and perform well in simulations.
  • This approach enhances the analysis of complex survival data for researchers and clinicians.