One straightforward metric to evaluate a survival prediction model is based on the Mean Absolute Error (MAE) -- the average of the absolute difference between the time predicted by the model and the true event time, over all subjects. Unfortunately, this is challenging because, in practice, the test set includes (right) censored individuals, meaning we do not know when a censored individual actually experienced the event. In this paper, we explore various metrics to estimate MAE for survival datasets that include (many) censored individuals. Moreover, we introduce a novel and effective approach for generating realistic semi-synthetic survival datasets to facilitate the evaluation of metrics. Our findings, based on the analysis of the semi-synthetic datasets, reveal that our proposed metric (MAE using pseudo-observations) is able to rank models accurately based on their performance, and often closely matches the true MAE -- in particular, is better than several alternative methods.

翻译：评估生存预测模型的一种直接指标基于平均绝对误差（MAE）——即模型预测时间与真实事件时间在所有受试者上的绝对差值的平均值。遗憾的是，这在实际应用中存在挑战，因为测试集包含（右）删失个体，即我们不知道删失个体实际发生事件的时间。本文探讨了多种估算包含（大量）删失个体的生存数据集MAE的方法。此外，我们提出了一种新颖且有效的方法，用于生成逼真的半合成生存数据集，以促进指标评估。我们的发现基于对半合成数据集的分析表明，我们提出的指标（使用伪观测的MAE）能够基于模型性能准确排序，并且常常与真实MAE高度吻合——特别是优于多种替代方法。