Factorial survival analysis for treatment effects under dependent censoring

TL;DR

This paper develops new factorial survival analysis methods that handle dependent censoring, extending existing techniques to more realistic scenarios, and demonstrates their effectiveness through simulations and real data applications.

Contribution

It introduces a novel approach combining factorial survival analysis with survival copula models to address dependent censoring in treatment effect studies.

Findings

Proposed F-test shows sound performance in simulations

Methods effectively handle dependent censoring scenarios

Implementation available in R package compound.Cox

Abstract

Factorial analyses offer a powerful nonparametric means to detect main or interaction effects among multiple treatments. For survival outcomes, e.g. from clinical trials, such techniques can be adopted for comparing reasonable quantifications of treatment effects. The key difficulty to solve in survival analysis concerns the proper handling of censoring. So far, all existing factorial analyses for survival data were developed under the independent censoring assumption, which is too strong for many applications. As a solution, the central aim of this article is to develop new methods in factorial survival analyses under quite general dependent censoring regimes. This will be accomplished by combining existing results for factorial survival analyses with techniques developed for survival copula models. As a result, we will present an appealing F-test that exhibits sound performance in our simulation study. The new methods are illustrated in real data analysis. We implement the proposed method in an R function surv.factorial(.) in the R package compound.Cox.