survivalContour: visualizing predicted survival via colored contour plots.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Bioinformatics advances Pub Date : 2024-07-25 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae105
Yushu Shi, Liangliang Zhang, Kim-Anh Do, Robert R Jenq, Christine B Peterson
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Abstract

Summary: Advances in survival analysis have facilitated unprecedented flexibility in data modeling, yet there remains a lack of tools for illustrating the influence of continuous covariates on predicted survival outcomes. We propose the utilization of a colored contour plot to depict the predicted survival probabilities over time. Our approach is capable of supporting conventional models, including the Cox and Fine-Gray models. However, its capability shines when coupled with cutting-edge machine learning models such as random survival forests and deep neural networks.

Availability and implementation: We provide a Shiny app at https://biostatistics.mdanderson.org/shinyapps/survivalContour/ and an R package available at https://github.com/YushuShi/survivalContour as implementations of this tool.

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survivalContour:通过彩色等高线图直观显示预测存活率。
摘要:生存分析技术的进步为数据建模带来了前所未有的灵活性,但目前仍缺乏说明连续协变量对预测生存结果影响的工具。我们建议使用彩色等值线图来描述随时间变化的预测生存概率。我们的方法能够支持传统模型,包括 Cox 和 Fine-Gray 模型。然而,当与随机生存森林和深度神经网络等前沿机器学习模型结合使用时,我们的方法将大放异彩:我们在 https://biostatistics.mdanderson.org/shinyapps/survivalContour/ 上提供了一个 Shiny 应用程序,并在 https://github.com/YushuShi/survivalContour 上提供了一个 R 软件包作为该工具的实现。
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