Over the last five decades, we have seen strong methodological advances in survival analysis, using parametric methods and, more prominently, methods based on non-/semi-parametric estimation. As the methodological landscape continues to evolve, the task of navigating through the multitude of methods and identifying available software resources is becoming increasingly challenging -- especially in more complex scenarios, such as when dealing with interval-censored or clustered survival data, non-proportional hazards, or dependent censoring. This tutorial explores the potential of using the framework of smooth transformation models for survival analysis in the R system for statistical computing. This framework provides a unified maximum-likelihood approach that covers a wide range of survival models, including well-established ones such as the Weibull model and a fully parametric version of the famous Cox proportional hazards model, and various extensions for more complex scenarios. We explore models for non-proportional/crossing hazards, dependent censoring, clustered observations and extensions towards personalised medicine within this framework. Using survival data from a two-arm randomised controlled trial on rectal cancer therapy, we demonstrate how survival analysis tasks can be seamlessly navigated in R within this framework using the implementation provided by the "tram" package, and few related packages.

翻译：过去五十年间，生存分析方法取得了显著进展，既包括参数方法，也包括更突出的非参数/半参数估计方法。随着方法学领域的持续演进，如何从众多方法中筛选适用技术并识别可用的软件资源，正变得日益困难——尤其是在处理区间删失或聚类生存数据、非比例风险以及相依删失等复杂场景时。本教程探讨了在R统计计算系统中运用平滑变换模型框架进行生存分析的潜力。该框架提供了一套统一的极大似然方法，涵盖从经典威布尔模型、著名Cox比例风险模型的完全参数化版本，到适用于复杂场景的各种扩展模型。我们重点研究了该框架下非比例/交叉风险、相依删失、聚类观测数据以及个性化医疗扩展模型。通过一项直肠癌治疗双盲随机对照试验的生存数据，本文演示了如何借助"tram"包及少数相关包的实现，在R语言中无缝完成生存分析任务。