CondiS web app: imputation of censored lifetimes for machine learning-based survival analysis.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
02 09 2022
02 09 2022
Historique:
received:
15
03
2022
revised:
27
05
2022
accepted:
07
07
2022
pubmed:
9
7
2022
medline:
15
11
2022
entrez:
8
7
2022
Statut:
ppublish
Résumé
In the era of big data, machine learning techniques are widely applied to every area in biomedical research including survival analysis. It is well recognized that censoring, which is a common missing issue in survival time data, hampers the direct usage of these machine learning techniques. Here, we present CondiS, a web toolkit with graphical user interface to help impute the survival times for censored observations and predict the survival times for future enrolled patients. CondiS imputes a censored survival time based on its distribution conditional on its observed part. When covariates are available, CondiS-X incorporates this information to further increase the imputation accuracy. Users can also upload data of newly enrolled patients and predict their survival times. As the first web-app tool with an imputation function for censored lifetime data, CondiS web can facilitate conducting survival analysis with machine learning approaches. CondiS is an open-source application implemented with Shiny in R, available free at: https://biostatistics.mdanderson.org/shinyapps/CondiS/. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 35801895
pii: 6633922
doi: 10.1093/bioinformatics/btac461
pmc: PMC9438949
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4252-4254Subventions
Organisme : NCI NIH HHS
ID : R03 CA270725
Pays : United States
Organisme : Cancer Prevention and Research Institute of Texas
ID : RR190079
Organisme : Recruitment of Established Investigators
Organisme : University of Texas MD Anderson Cancer Center
Informations de copyright
© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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