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

Yunwei Zhang, Germaine Wong, Graham Mann, Samuel Muller, Jean Y. H. Yang*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    6 Citations (Scopus)
    25 Downloads (Pure)

    Abstract

    Survival analysis is a branch of statistics that deals with both the tracking of time and 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 binarize the survival time and perform a classification analysis. Here, we develop a benchmarking design, SurvBenchmark, that evaluates a diverse collection of survival models for both clinical and omics data sets. SurvBenchmark not only focuses on classical approaches such as the Cox model but also evaluates state-of-the-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 design with 320 comparisons (20 methods over 16 data sets) shows that the performances of survival models vary in practice over real-world data sets 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.

    Original languageEnglish
    Pages (from-to)1-13
    Number of pages13
    JournalGigaScience
    Volume11
    DOIs
    Publication statusPublished - 2022

    Bibliographical note

    Copyright © 2022 The Author(s). Published by Oxford University Press GigaScience. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

    Keywords

    • machine learning
    • survival analysis
    • survival prediction

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