Review of Statistical Methods for Evaluating the Performance of Survival or Other Time-to-Event Prediction Models (from Conventional to Deep Learning Approaches)

• Published in: Korean Journal of Radiology Vol. 22; no. 10; pp. 1697 - 1707
• Main Authors: Park, Seo Young, Park, Ji Eun, Kim, Hyungjin, Park, Seong Ho
• Format: Journal Article Book Review
• Language: English
• Published: Seoul The Korean Society of Radiology 01.10.2021; 대한영상의학회
• Subjects: Deep learning; Medical research; Patients; Performance evaluation; Statistical methods; Survival analysis; Technology, Experiment, and Physics; 방사선과학
• ISSN: 1229-6929; 2005-8330
• DOI: 10.3348/kjr.2021.0223

The recent introduction of various high-dimensional modeling methods, such as radiomics and deep learning, has created a much greater diversity in modeling approaches for survival prediction (or, more generally, time-to-event prediction). The newness of the recent modeling approaches and unfamiliarity with the model outputs may confuse some researchers and practitioners about the evaluation of the performance of such models. Methodological literacy to critically appraise the performance evaluation of the models and, ideally, the ability to conduct such an evaluation would be needed for those who want to develop models or apply them in practice. This article intends to provide intuitive, conceptual, and practical explanations of the statistical methods for evaluating the performance of survival prediction models with minimal usage of mathematical descriptions. It covers from conventional to deep learning methods, and emphasis has been placed on recent modeling approaches. This review article includes straightforward explanations of C indices (Harrell's C index, etc.), time-dependent receiver operating characteristic curve analysis, calibration plot, other methods for evaluating the calibration performance, and Brier score.