Abstract

Background Survival analysis is a cornerstone of medical research, enabling the assessment of clinical outcomes for disease progression and treatment efficiency. Despite its central importance, no commonly used spreadsheet software can handle survival analysis and there is no web server available for its computation. Objective Here, we introduce a web-based tool capable of performing univariate and multivariate Cox proportional hazards survival analysis using data generated by genomic, transcriptomic, proteomic, or metabolomic studies. Methods We implemented different methods to establish cut-off values for the trichotomization or dichotomization of continuous data. The false discovery rate is computed to correct for multiple hypothesis testing. A multivariate analysis option enables comparing omics data with clinical variables. Results We established a registration-free web-based survival analysis tool capable of performing univariate and multivariate survival analysis using any custom-generated data. Conclusions This tool fills a gap and will be an invaluable contribution to basic medical and clinical research.

Keywords

UnivariateWeb applicationMultivariate analysisComputer scienceMultivariate statisticsProportional hazards modelData miningSurvival analysisData scienceMedicineWorld Wide WebMachine learningInternal medicine

MeSH Terms

Biomedical ResearchHumansInternetProteomicsSoftwareSurvival Analysis

Affiliated Institutions

Related Publications

Publication Info

Year
2021
Type
article
Volume
23
Issue
7
Pages
e27633-e27633
Citations
1690
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

1690
OpenAlex
58
Influential
1250
CrossRef

Cite This

András Lánczky, Balázs Győrffy (2021). Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. Journal of Medical Internet Research , 23 (7) , e27633-e27633. https://doi.org/10.2196/27633

Identifiers

DOI
10.2196/27633
PMID
34309564
PMCID
PMC8367126

Data Quality

Data completeness: 86%