Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Int J Epidemiol. 2023 Dec 25;52(6):1984-1989. doi: 10.1093/ije/dyad122.
Providing a dynamic assessment of prognosis is essential for improved personalized medicine. The landmark model for survival data provides a potentially powerful solution to the dynamic prediction of disease progression. However, a general framework and a flexible implementation of the model that incorporates various outcomes, such as competing events, have been lacking. We present an R package, dynamicLM, a user-friendly tool for the landmark model for the dynamic prediction of survival data under competing risks, which includes various functions for data preparation, model development, prediction and evaluation of predictive performance.
dynamicLM as an R package.
The package includes options for incorporating time-varying covariates, capturing time-dependent effects of predictors and fitting a cause-specific landmark model for time-to-event data with or without competing risks. Tools for evaluating the prediction performance include time-dependent area under the ROC curve, Brier Score and calibration.
Available on GitHub [https://github.com/thehanlab/dynamicLM].
提供预后的动态评估对于改进个性化医学至关重要。生存数据的里程碑模型为疾病进展的动态预测提供了一个潜在强大的解决方案。然而，缺乏一种通用的框架和灵活的模型实现，该模型可以结合各种结果，如竞争事件。我们提出了一个 R 包 dynamicLM，这是一个用于在竞争风险下对生存数据进行动态预测的里程碑模型的用户友好工具，它包括用于数据准备、模型开发、预测和评估预测性能的各种功能。
dynamicLM 作为一个 R 包。
该包包括了纳入时变协变量、捕捉预测因子的时变效应以及拟合具有或不具有竞争风险的时间相依性的里程碑模型的选项。用于评估预测性能的工具包括时间依赖性 ROC 曲线下面积、Brier 得分和校准。
可在 GitHub [https://github.com/thehanlab/dynamicLM] 上获得。
