Survival of the Fittest Group: Factorial Analyses of Treatment Effects under Independent Right-Censoring

TL;DR

This paper develops new effect parameters and statistical tests for factorial survival analysis with right-censored data, providing rigorous asymptotic theory, bootstrap methods, and practical applications in medical research.

Contribution

It introduces novel effect parameters for factorial survival designs, along with estimation and testing procedures, extending existing methods to more complex experimental setups.

Findings

New effect parameters for factorial survival analysis

Asymptotic properties established via empirical process techniques

Method demonstrated on colon cancer patient data

Abstract

This paper introduces new effect parameters for factorial survival designs with possibly right-censored time-to-event data. In the special case of a two-sample design it coincides with the concordance or Wilcoxon parameter in survival analysis. More generally, the new parameters describe treatment or interaction effects and we develop estimates and tests to infer their presence. We rigorously study the asymptotic properties by means of empirical process techniques and additionally suggest wild bootstrapping for a consistent and distribution-free application of the inference procedures. The small sample performance is discussed based on simulation results. The practical usefulness of the developed methodology is exemplified on a data example about patients with colon cancer by conducting one- and two-factorial analyses.