论文标题
阳性未标记生存数据分析
Positive-Unlabelled Survival Data Analysis
论文作者
论文摘要
在本文中，我们考虑了一个新型的未标记数据框架，其中在观察时间内有事件的受试者视为正数据的正数据存活时间，作为正数据，并且观察到未标记的数据检查时间，但事件是否发生在某些受试者中是未知的。我们考虑两种情况：（1）当在正数据中观察到检查时间时，以及（2）未观察到。在这两种情况下，我们都开发了参数模型，非参数模型和机器学习模型以及这些模型的估计策略。仿真研究表明，在此数据设置下，传统的生存分析可能会产生严重偏见的结果，而拟议的估计方法可以提供有效的结果。
In this paper, we consider a novel framework of positive-unlabeled data in which as positive data survival times are observed for subjects who have events during the observation time as positive data and as unlabeled data censoring times are observed but whether the event occurs or not are unknown for some subjects. We consider two cases: (1) when censoring time is observed in positive data, and (2) when it is not observed. For both cases, we developed parametric models, nonparametric models, and machine learning models and the estimation strategies for these models. Simulation studies show that under this data setup, traditional survival analysis may yield severely biased results, while the proposed estimation method can provide valid results.
