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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.
From: Sandra Siegfried [view email]
[v1]
Fri, 9 Feb 2024 14:16:29 UTC (123 KB)
[v2]
Sun, 23 Mar 2025 15:05:17 UTC (118 KB)
[v3]
Sat, 6 Jun 2026 10:01:33 UTC (283 KB)
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