TL;DR
This paper revisits the comparison of survival curves in immuno-oncology, highlighting issues with traditional hazard ratio interpretations and proposing permutation tests for more efficient, accurate analysis especially with delayed effects.
Contribution
It introduces a permutation test approach for survival analysis that improves efficiency and control of false positives in non-proportional hazards scenarios.
Findings
Permutation tests enhance power in delayed effect scenarios.
Traditional hazard ratio interpretations can lead to misleading conclusions.
Sample sizes can be reduced without sacrificing statistical power.
Abstract
A fundamental concept in two-arm non-parametric survival analysis is the comparison of observed versus expected numbers of events on one of the treatment arms (the choice of which arm is arbitrary), where the expectation is taken assuming that the true survival curves in the two arms are identical. This concept is at the heart of the counting-process theory that provides a rigorous basis for methods such as the log-rank test. It is natural, therefore, to maintain this perspective when extending the log-rank test to deal with non-proportional hazards, for example by considering a weighted sum of the "observed - expected" terms, where larger weights are given to time periods where the hazard ratio is expected to favour the experimental treatment. In doing so, however, one may stumble across some rather subtle issues, related to the difficulty in ascribing a causal interpretation to hazard…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
