Computationally Efficient Implementation of a Novel Algorithm for the General Unified Threshold Model of Survival (GUTS)

The General Unified Threshold model of Survival (GUTS) provides a consistent mathematical framework for survival analysis. However, the calibration of GUTS models is computationally challenging. We present a novel algorithm and its fast implementation in our R package, GUTS, that help to overcome these challenges. We show a step-by-step application example consisting of model calibration and uncertainty estimation as well as making probabilistic predictions and validating the model with new data. Using self-defined wrapper functions, we show how to produce informative text printouts and plots without effort, for the inexperienced as well as the advanced user. The complete ready-to-run script is available as supplemental material. We expect that our software facilitates novel re-analysis of existing survival data as well as asking new research questions in a wide range of sciences. In particular the ability to quickly quantify stressor thresholds in conjunction with dynamic compensating processes, and their uncertainty, is an improvement that complements current survival analysis methods.通用生存统一阈值模型（General Unified Threshold model of Survival，GUTS）为生存分析提供了一套统一的数学框架。然而，GUTS模型的校准工作面临着计算层面的挑战。本文提出一种全新算法，并将其快速实现于我们开发的R语言包GUTS中，可有效攻克上述计算难题。本文展示了一则分步应用示例，涵盖模型校准、不确定性估计、概率预测以及利用新数据对模型进行验证等完整环节。通过自定义封装函数，本文演示了如何轻松生成信息详实的文本输出与可视化图表，可兼顾入门与资深用户的使用需求。完整的可直接运行脚本可作为补充材料获取。我们期望本软件能够助力现有生存数据的全新再分析，并为多学科领域的全新研究问题探索提供支持。尤其值得一提的是，本软件可快速量化胁迫因子阈值及其伴随的动态补偿过程与不确定性，这一改进可作为现有生存分析方法的有力补充。