The super learner for time-to-event outcomes: A tutorial
事件发生时间结局的超级学习器：实践教程AI 解读 ↓
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事件发生时间结局的超级学习器：实践教程
- 研究问题
- 面对删失的事件发生时间数据，如何从预设候选模型中选择或组合学习器，以估计个体特征条件下的风险或生存概率？
- 方法与思路
- 作者介绍超级学习器的一般步骤，分别讲解一种离散时间和两种连续时间实现，并用公开数据与 R 代码演示。
- 材料陈述的贡献
- 摘要所述贡献是提供易于实践的教程，比较三种实现的性质并说明软件使用方式，而非明确提出新的识别或估计理论。
- 阅读关注点
- 应核对删失处理、评价损失和交叉验证的实现，以及三种时间尺度方案的适用对象、候选库选择与代码复现细节。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收均未核实；摘要未说明删失假设或比较结果，不能据此认定某种实现普遍更优。
原文摘要
Estimating risks or survival probabilities conditional on individual characteristics based on censored time-to-event data is a commonly faced task. This may be for the purpose of developing a prediction model or may be part of a wider estimation procedure, such as in causal inference. A challenge is that it is impossible to know at the outset which of a set of candidate models will provide the best risk estimates. The super learner is a powerful approach for finding the best model or combination of models ('ensemble') among a pre-specified set of candidate models or 'learners', which can include both 'statistical' models (e.g. parametric, semi-parametric models) and 'machine learning' models. Super learners for time-to-event outcomes have been developed, but the literature is technical and the full details of how these methods work and can be implemented in practice have not previously been presented in an accessible format. In this paper we provide a practical tutorial on super learner methods for time-to-event outcomes. An overview of the general steps involved in the super learner is given, followed by details of three specific implementations for time-to-event outcomes. These include the originally proposed super learner, which involves using a discrete time scale, and two more recently proposed versions of the super learner for continuous-time data. We compare the properties of the methods and provide information on how they can be implemented in R. The methods are illustrated using an open access data set and R code is provided.