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.

翻译：因子分析提供了一种强大的非参数方法，用于检测多种处理间的主效应或交互效应。对于生存结局（例如来自临床试验的数据），此类技术可用于比较处理效应的合理量化指标。生存分析中需解决的关键困难在于如何恰当处理删失。迄今为止，所有现有的生存数据因子分析均在独立删失假设下发展，这一假设在许多应用中过于严格。为解决此问题，本文的核心目标是开发在相当一般的依赖删失机制下进行因子生存分析的新方法。这将通过结合现有因子生存分析结果与生存Copula模型技术来实现。最终，我们提出一种具有吸引力的F检验，该检验在模拟研究中表现出稳健性能。新方法在真实数据分析中得到验证。我们在R包compound.Cox中的函数surv.factorial()中实现了所提出的方法。