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:对于生存结果,例如临床试验的结果,可以采用这种技术来比较治疗效果的合理量化。在生存分析中需要解决的关键困难在于对审查的适当处理。到目前为止,所有现有的生存数据因素分析都是根据独立审查假设进行的,对于许多应用来说,这种假设太强。作为解决办法,本条的中心目的是在相当一般的依赖性审查制度下,在要素生存分析中制定新方法。这将通过将参数生存分析的现有结果与为生存性杂草模型开发的技术结合起来来实现。结果,我们将在模拟研究中提出一个具有说服力的F测试,以显示良好的性能。新的方法在真实的数据分析中加以说明。我们在R包化合物的R函数surv.factor () 中应用了拟议的方法。Cox。