2023/06/01 by Shi-ang Qi, Qi, Shi-ang, Neeraj Kumar +13 · 6 citations
Computer Science · Mathematics · Social Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Insurance, Mortality, Demography, Risk Management #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2306.01196
openalex publication_date 2023/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.