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Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation
by
Győrffy, Balázs
, Lánczky, András
in
Analysis
/ Clinical assessment
/ Clinical outcomes
/ Clinical research
/ Clinical variables
/ Computation
/ Continuous data
/ Data
/ Gene expression
/ Genomics
/ Hypotheses
/ Hypothesis testing
/ Implementation
/ Internet
/ Mathematical functions
/ Medical prognosis
/ Medical research
/ Multivariate analysis
/ Original Paper
/ Registration
/ Software packages
/ Survival analysis
/ Treatment outcomes
/ Variables
2021
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Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation
by
Győrffy, Balázs
, Lánczky, András
in
Analysis
/ Clinical assessment
/ Clinical outcomes
/ Clinical research
/ Clinical variables
/ Computation
/ Continuous data
/ Data
/ Gene expression
/ Genomics
/ Hypotheses
/ Hypothesis testing
/ Implementation
/ Internet
/ Mathematical functions
/ Medical prognosis
/ Medical research
/ Multivariate analysis
/ Original Paper
/ Registration
/ Software packages
/ Survival analysis
/ Treatment outcomes
/ Variables
2021
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Do you wish to request the book?
Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation
by
Győrffy, Balázs
, Lánczky, András
in
Analysis
/ Clinical assessment
/ Clinical outcomes
/ Clinical research
/ Clinical variables
/ Computation
/ Continuous data
/ Data
/ Gene expression
/ Genomics
/ Hypotheses
/ Hypothesis testing
/ Implementation
/ Internet
/ Mathematical functions
/ Medical prognosis
/ Medical research
/ Multivariate analysis
/ Original Paper
/ Registration
/ Software packages
/ Survival analysis
/ Treatment outcomes
/ Variables
2021
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Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation
Journal Article
Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation
2021
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Overview
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.
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