# A Multiple kernel testing procedure for non-proportional hazards in factorial designs

arXiv: https://arxiv.org/abs/2206.07239 · HTML: https://mlsanity.com/paper/2206.07239/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 45 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [A Kernel Test of Goodness of Fit](https://mlsanity.com/paper/1602.02964/) (2016)
- [A Kernel Independence Test for Random Processes](https://mlsanity.com/paper/1402.4501/) (2014)
- [Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data](https://mlsanity.com/paper/2008.08397/) (2020)
- [A maximum-mean-discrepancy goodness-of-fit test for censored data](https://mlsanity.com/paper/1810.04286/) (2018)
- [A Reproducing Kernel Hilbert Space log-rank test for the two-sample problem](https://mlsanity.com/paper/1904.05187/) (2019)
- [Inferring median survival differences in general factorial designs via permutation tests](https://mlsanity.com/paper/2006.14316/) (2020)

## Built on by (most cited first, 1 of 1)

- [A general framework for the analysis of kernel-based tests](https://mlsanity.com/paper/2209.00124/) (2022)

## Abstract (arXiv)

In this paper we propose a Multiple kernel testing procedure to infer survival data when several factors (e.g. different treatment groups, gender, medical history) and their interaction are of interest simultaneously. Our method is able to deal with complex data and can be seen as an alternative to the omnipresent Cox model when assumptions such as proportionality cannot be justified. Our methodology combines well-known concepts from Survival Analysis, Machine Learning and Multiple Testing: differently weighted log-rank tests, kernel methods and multiple contrast tests. By that, complex hazard alternatives beyond the classical proportional hazard set-up can be detected. Moreover, multiple comparisons are performed by fully exploiting the dependence structure of the single testing procedures to avoid a loss of power. In all, this leads to a flexible and powerful procedure for factorial survival designs whose theoretical validity is proven by martingale arguments and the theory for V-statistics. We evaluate the performance of our method in an extensive simulation study and illustrate it by a real data analysis.