Over the last five decades, we have seen strong methodological advances in survival analysis, mainly in two separate strands: One strand is based on a parametric approach that assumes some response distribution. More prominent, however, is the strand of flexible methods which rely mainly on non-/semi-parametric estimation. As the methodological landscape continues to evolve, the task of navigating through the multitude of methods and identifying corresponding available software resources is becoming increasingly difficult. This task becomes particularly challenging in more complex scenarios, such as when dealing with interval-censored or clustered survival data, non-proportionality, or dependent censoring. In this tutorial, we explore the potential of using smooth transformation models for survival analysis in the R system for statistical computing. These models provide a unified maximum likelihood framework that covers a range of survival models, including well-established ones such as the Weibull model and a fully parameterised version of the famous Cox proportional hazards model, as well as extensions to more complex scenarios. We explore smooth transformation models for non-proportional/crossing hazards, dependent censoring, clustered observations and extensions towards personalised medicine within this framework. By fitting these models to survival data from a two-arm randomised controlled trial on rectal cancer therapy, we demonstrate how survival analysis tasks can be seamlessly navigated within the smooth transformation model framework in R. This is achieved by the implementation provided by the "tram" package and few related packages.

翻译：过去五十年间，生存分析领域取得了显著的方法学进展，主要沿两条相互独立的研究路径展开：一条路径基于参数化方法，假定某种响应分布；而更为突出的则是灵活方法路径，主要依赖非参数/半参数估计。随着方法学版图的持续演进，在众多方法中进行导航并识别相应可用软件资源的任务正变得日益艰巨。在处理区间删失或聚类生存数据、非比例性假设或相依删失等复杂场景时，这一挑战尤为突出。本教程系统探讨了在R统计计算系统中运用平滑变换模型进行生存分析的潜力。这类模型提供了一个统一的最大似然框架，涵盖多种生存模型，既包括威布尔模型等经典模型和著名Cox比例风险模型的完全参数化版本，也延伸至更复杂场景。我们在此框架下探索了用于处理非比例/交叉风险、相依删失、聚类观测及个性化医学推广的平滑变换模型。通过将此类模型拟合至一项关于直肠癌治疗的双臂随机对照试验的生存数据，我们展示了如何在R环境下借助平滑变换模型框架无缝完成生存分析任务。这一实践得益于"tram"包及其若干关联包提供的算法实现。