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CC BY V4.0, Open Access
Journal article
SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data
Gigascience, Vol.11, giac071
2022
PMID: 35906887
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.
Details
- Title
- SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data
- Authors/Creators
- Yunwei ZhangGermaine Wong - The University of SydneyGraham Mann - Australian National UniversitySamuel Muller - The University of SydneyJean Yee Hwa Yang - The University of Sydney
- Publication Details
- Gigascience, Vol.11, giac071
- Publisher
- Oxford University Press; OXFORD
- Number of pages
- 13
- Grant note
- Australian Research Council: DP210100521 National Health and Medical Research Council's CRE: APP1135285
The following sources of funding for each author, and for the manuscript preparation, are gratefully acknowledged: Australian Research Council Discovery Project grant (DP210100521) to SM, National Health and Medical Research Council's CRE (APP1135285) to JYHY and GJM, AIR@innoHK programme of the Innovation and Technology Commission of Hong Kong to JYHY, and Research Training Program Tuition Fee Offset and Stipend Scholarship and the Dean's International Postgraduate Research Scholarship (DIPRS) to YZ. The funding source had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the manuscript; and in the decision to submit the manuscript for publication.
- Identifiers
- 991005707667307891
- Copyright
- © The Author(s) 2022.
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
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- Citation topics
- 9 Mathematics
- 9.92 Statistical Methods
- 9.92.220 Robust Estimation
- Web Of Science research areas
- Biology
- ESI research areas
- Social Sciences, general