ArticleGigaScience2022
SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data.
Article in GigaScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 45 citations in OpenAlex.
- Predicting incident type 2 diabetes using wearable activity and polygenic risk: A survival-modeling study in All of Us.The Journal of clinical endocrinology and metabolism · 2026Article
- A large-scale neutral comparison study of survival models on low-dimensional data.Bioinformatics (Oxford, England) · 2026Article
- DKC1: a robust prognostic factor and potential therapeutic target in multiple myeloma.Journal of translational medicine · 2026Article
- Deep learning predicts onset acceleration of 38 age-associated diseases from blood and body composition biomarkers in the UK Biobank.GeroScience · 2026Article
- Bio-Inspired Artificial Intelligence Multimorbidity Score for Human-Machine Clinical Risk Stratification.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Random survival forests for the analysis of recurrent events for right-censored data, with or without a terminal event.BMC medical research methodology · 2025Article
- Two-stage sampling for better survival model performance.BMC medical research methodology · 2025Article
- Distinct perturbances in metabolic pathways associate with disease progression in inflammatory bowel disease.Journal of Crohn's & colitis · 2025Observational
- A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables.Briefings in bioinformatics · 2025Review
- A Distinguished Roadmap of Fibroblast Senescence in Predicting Immunotherapy Response and Prognosis Across Human Cancers.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Network-based transfer of pan-cancer immunotherapy responses to guide breast cancer prognosis.NPJ systems biology and applications · 2025Article
- Machine learning-based tumor associated macrophages polarity signature predicts prognosis and treatment response in hepatocellular carcinoma.Frontiers in immunology · 2025Article
- Movement Clearing Screens for Military Service Member Musculoskeletal Injury Risk Identification.Journal of athletic training · 2025Article
- On the estimation of inverse-probability-of-censoring weights for the evaluation of survival prediction error.PloS one · 2025Article
- Interpretable deep learning survival predictions in sporadic Creutzfeldt-Jakob disease.Journal of neurology · 2024Article
- sparsesurv: a Python package for fitting sparse survival models via knowledge distillation.Bioinformatics (Oxford, England) · 2024Article
- Pan-cancer analysis implicates novel insights of lactate metabolism into immunotherapy response prediction and survival prognostication.Journal of experimental & clinical cancer research : CR · 2024Article
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- Statistical methods and resources for biomarker discovery using metabolomics.BMC bioinformatics · 2023Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.