Inferring median survival differences in general factorial designs via permutation tests

TL;DR

This paper introduces permutation tests for median survival differences in factorial designs with censored data, providing a robust alternative to hazard ratio-based methods especially under non-proportional hazards.

Contribution

It proposes permutation-based inference for median survival differences, offering a valid and broadly applicable method beyond traditional hazard ratio approaches.

Findings

Permutation tests control type-1 error effectively.

Methods demonstrate high power in simulations.

Applicable to various factorial survival scenarios.

Abstract

Factorial survival designs with right-censored observations are commonly inferred by Cox regression and explained by means of hazard ratios. However, in case of non-proportional hazards, their interpretation can become cumbersome; especially for clinicians. We therefore offer an alternative: median survival times are used to estimate treatment and interaction effects and null hypotheses are formulated in contrasts of their population versions. Permutation-based tests and confidence regions are proposed and shown to be asymptotically valid. Their type-1 error control and power behavior are investigated in extensive simulations, showing the new methods' wide applicability. The latter is complemented by an illustrative data analysis.