In this paper we propose a boosting algorithm to extend the applicability of a first hitting time model to high-dimensional frameworks. Based on an underlying stochastic process, first hitting time models do not require the proportional hazards assumption, hardly verifiable in the high-dimensional context, and represent a valid parametric alternative to the Cox model for modelling time-to-event responses. First hitting time models also offer a natural way to integrate low-dimensional clinical and high-dimensional molecular information in a prediction model, that avoids complicated weighting schemes typical of current methods. The performance of our novel boosting algorithm is illustrated in three real data examples.

在本文中，我们提出了一种增强算法，以将首次命中时间模型的适用性扩展到高维框架。基于潜在的随机过程，首次命中时间模型不需要比例风险假设，在高维环境中难以验证，并且代表 Cox 模型的有效参数替代方案，用于对事件响应时间进行建模。首次命中时间模型还提供了一种将低维临床和高维分子信息整合到预测模型中的自然方式，避免了当前方法典型的复杂加权方案。三个真实数据示例说明了我们新颖的提升算法的性能。