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. 2021 Jul 26;23(7):e27633.
doi: 10.2196/27633.

Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation

András Lánczky  1   2 , Balázs Győrffy  1   2
Affiliations

Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation

András Lánczky et al. J Med Internet Res. .

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: Cox regression; Kaplan-Meier plot; follow-up; internet; multivariate analysis; survival.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Kaplan-Meier curves showing main concepts used in survival analysis, including the (A) hazard rate (high/low) and (B) median survival. The green arrow shows the visually determined median survival and the blue arrow shows the survival probability at 50 months.
Figure 2
Figure 2
A cut-off plot can be used to visualize the correlation between the used cut-off values and the achieved P values (A) and hazard rate (HR) (B). The red circle identifies the best cutoff. The computation of false discovery rate across all P values provides correction for multiple hypothesis testing.
See this image and copyright information in PMC

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