ArticleGenomics & informatics2019
Review of statistical methods for survival analysis using genomic data.
Article in Genomics & informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.Frontiers in artificial intelligence · 2024Pooled it
- Innovations in biomarker stratification for precision oncology.Clinical and experimental medicine · 2026Review
- Enhancing multimodal survival prediction: tri-modal learning with clinical knowledge integration via state space models.Frontiers in oncology · 2026Article
- Improved prediction of MAPKi response duration in melanoma patients using genomic data and machine learning.NPJ precision oncology · 2025Article
- Developing clinical prognostic models to predict graft survival after renal transplantation: comparison of statistical and machine learning models.BMC medical informatics and decision making · 2025Article
- Article
- Delayed kernels for longitudinal survival analysis and dynamic prediction.Statistical methods in medical research · 2024Article
- Optimizing Prognostic Predictions in Liver Cancer with Machine Learning and Survival Analysis.Entropy (Basel, Switzerland) · 2024Article
- Making Sense of Censored Covariates: Statistical Methods for Studies of Huntington's Disease.Annual review of statistics and its application · 2024Article
- CenTime: Event-conditional modelling of censoring in survival analysis.Medical image analysis · 2024Article
- Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021-2023 Literature.Biology · 2023Article
- An artificial intelligence prediction model based on extracellular matrix proteins for the prognostic prediction and immunotherapeutic evaluation of ovarian serous adenocarcinoma.Frontiers in molecular biosciences · 2023Article
- CAMR: cross-aligned multimodal representation learning for cancer survival prediction.Bioinformatics (Oxford, England) · 2023Article
- Estimating the Prognosis of Low-Grade Glioma with Gene Attention Using Multi-Omics and Multi-Modal Schemes.Biology · 2022Article
- Efficient and accurate frailty model approach for genome-wide survival association analysis in large-scale biobanks.Nature communications · 2022Article
- SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data.GigaScience · 2022Article
- An Ensemble Deep Learning Model with a Gene Attention Mechanism for Estimating the Prognosis of Low-Grade Glioma.Biology · 2022Article
- Novel Application of Survival Models for Predicting Microbial Community Transitions with Variable Selection for Environmental DNA.Applied and environmental microbiology · 2022Article
- Article
- Prediction of Bronchopneumonia Inpatients' Total Hospitalization Expenses Based on BP Neural Network and Support Vector Machine Models.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains "censored" data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing "omics" data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.
Indexed as
Identifiers
What OpenQuestion holds
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.