The Fine-Gray proportional sub-distribution hazards (PSH) model is among the most popular regression model for competing risks time-to-event data. This article develops a fast safe feature elimination method, named PSH-SAFE, for fitting the penalized Fine-Gray PSH model with a Lasso (or adaptive Lasso) penalty. Our PSH-SAFE procedure is straightforward to implement, fast, and scales well to ultrahigh dimensional data. We also show that as a feature screening procedure, PSH-SAFE is safe in a sense that the eliminated features are guaranteed to be inactive features in the original Lasso (or adaptive Lasso) estimator for the penalized PSH model. We evaluate the performance of the PSH-SAFE procedure in terms of computational efficiency, screening efficiency and safety, run-time, and prediction accuracy on multiple simulated datasets and a real bladder cancer data. Our empirical results show that the PSH-SAFE procedure possesses desirable screening efficiency and safety properties and can offer substantially improved computational efficiency as well as similar or better prediction performance in comparison to their baseline competitors.

中文翻译：

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超高维协变量细灰色竞争风险模型的快速套索式安全筛选


细灰色比例子分布风险 (PSH) 模型是竞争风险事件时间数据最流行的回归模型之一。本文开发了一种名为 PSH-SAFE 的快速安全特征消除方法，用于使用 Lasso（或自适应 Lasso）惩罚来拟合受惩罚的 Fine-Gray PSH 模型。我们的 PSH-SAFE 程序实施起来简单、快速，并且可以很好地扩展到超高维数据。我们还表明，作为一种特征筛选过程，PSH-SAFE 在某种意义上是安全的，因为被消除的特征保证是惩罚 PSH 模型的原始 Lasso（或自适应 Lasso）估计器中的非活动特征。我们在多个模拟数据集和真实膀胱癌数据上的计算效率、筛选效率和安全性、运行时间以及预测准确性方面评估 PSH-SAFE 程序的性能。我们的实证结果表明，PSH-SAFE 程序具有理想的筛选效率和安全特性，与基准竞争对手相比，可以显着提高计算效率以及类似或更好的预测性能。