In survival analysis, prediction models are needed as stand-alone tools and in applications of causal inference to estimate nuisance parameters. The super learner is a machine learning algorithm which combines a library of prediction models into a meta learner based on cross-validated loss. In right-censored data, the choice of the loss function and the estimation of the expected loss need careful consideration. We introduce the state learner, a new super learner for survival analysis, which simultaneously evaluates libraries of prediction models for the event of interest and the censoring distribution. The state learner can be applied to all types of survival models, works in the presence of competing risks, and does not require a single pre-specified estimator of the conditional censoring distribution. We establish an oracle inequality for the state learner and investigate its performance through numerical experiments. We illustrate the application of the state learner with prostate cancer data, as a stand-alone prediction tool, and, for causal inference, as a way to estimate the nuisance parameter models of a smooth statistical functional.

翻译：在生存分析中，预测模型既可作为独立工具使用，也可在因果推断中用于估计冗余参数。超级学习器是一种机器学习算法，它基于交叉验证损失将一组预测模型库组合成元学习器。对于右删失数据，损失函数的选择与期望损失的估计需审慎考量。本文提出状态学习器——一种用于生存分析的新型超级学习器，它能同步评估目标事件预测模型库与删失分布预测模型库。该学习器适用于所有类型的生存模型，可在竞争风险存在的情况下工作，且无需预先指定条件删失分布的单一估计量。我们建立了状态学习器的oracle不等式，并通过数值实验检验其性能。以前列腺癌数据为例，我们演示了状态学习器作为独立预测工具的应用，以及在因果推断中作为估计平滑统计函数冗余参数模型的方法。