SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data

Abstract

AbstractSurvival analysis is a branch of statistics that deals with both, the tracking of time and of the survival status simultaneously as the dependent response. Current comparisons of survival model performance mostly center on clinical data with classic statistical survival models, with prediction accuracy often serving as the sole metric of model performance. Moreover, survival analysis approaches for censored omics data have not been thoroughly investigated. The common approach is to binarise the survival time and perform a classification analysis.Here, we develop a benchmarking framework, SurvBenchmark, that evaluates a diverse collection of survival models for both clinical and omics datasets. SurvBenchmark not only focuses on classical approaches such as the Cox model, but it also evaluates state-of-art machine learning survival models. All approaches were assessed using multiple performance metrics, these include model predictability, stability, flexibility and computational issues. Our systematic comparison framework with over 320 comparisons (20 methods over 16 datasets) shows that the performances of survival models vary in practice over real-world datasets and over the choice of the evaluation metric. In particular, we highlight that using multiple performance metrics is critical in providing a balanced assessment of various models. The results in our study will provide practical guidelines for translational scientists and clinicians, as well as define possible areas of investigation in both survival technique and benchmarking strategies.Contactjean.yang@sydney.edu.au