论文标题
依赖审查的基于Copula的增强模型
A copula-based boosting model for time-to-event prediction with dependent censoring
论文作者
论文摘要
事件时间分析的特征特征是对事件时间的审查。用于处理审查数据的大多数统计学习方法都受到独立审查的假设而受到限制，即使当假设不成立时可能会导致偏见的预测。本文介绍了Clayton-Boost，这是一种基于加速故障时间模型的增强方法，该方法使用Clayton Copula来处理事件和审查分布之间的依赖关系。通过利用副群体，不再需要独立的审查假设。在与常用方法的比较过程中，克莱顿 - 助推器显示出在依赖审查的存在下消除预测偏见的强大能力，如果依赖性强度或百分比审查率相当可观，则胜过比较方法。 Clayton-Boost的令人鼓舞的表现表明，确实有理由对独立审查假设至关重要，而现实世界中的数据可以从建模潜在的依赖性中受益。
A characteristic feature of time-to-event data analysis is possible censoring of the event time. Most of the statistical learning methods for handling censored data are limited by the assumption of independent censoring, even if this can lead to biased predictions when the assumption does not hold. This paper introduces Clayton-boost, a boosting approach built upon the accelerated failure time model, which uses a Clayton copula to handle the dependency between the event and censoring distributions. By taking advantage of a copula, the independent censoring assumption is not needed any more. During comparisons with commonly used methods, Clayton-boost shows a strong ability to remove prediction bias at the presence of dependent censoring and outperforms the comparing methods either if the dependency strength or percentage censoring are considerable. The encouraging performance of Clayton-boost shows that there is indeed reasons to be critical about the independent censoring assumption, and that real-world data could highly benefit from modelling the potential dependency.
