KMWin--a convenient tool for graphical presentation of results from Kaplan-Meier survival time analysis.

Abstract:

BACKGROUND: Analysis of clinical studies often necessitates multiple graphical representations of the results. Many professional software packages are available for this purpose. Most packages are either only commercially available or hard to use especially if one aims to generate or customize a huge number of similar graphical outputs. We developed a new, freely available software tool called KMWin (Kaplan-Meier for Windows) facilitating Kaplan-Meier survival time analysis. KMWin is based on the statistical software environment R and provides an easy to use graphical interface. Survival time data can be supplied as SPSS (sav), SAS export (xpt) or text file (dat), which is also a common export format of other applications such as Excel. Figures can directly be exported in any graphical file format supported by R. RESULTS: On the basis of a working example, we demonstrate how to use KMWin and present its main functions. We show how to control the interface, customize the graphical output, and analyse survival time data. A number of comparisons are performed between KMWin and SPSS regarding graphical output, statistical output, data management and development. Although the general functionality of SPSS is larger, KMWin comprises a number of features useful for survival time analysis in clinical trials and other applications. These are for example number of cases and number of cases under risk within the figure or provision of a queue system for repetitive analyses of updated data sets. Moreover, major adjustments of graphical settings can be performed easily on a single window. CONCLUSIONS: We conclude that our tool is well suited and convenient for repetitive analyses of survival time data. It can be used by non-statisticians and provides often used functions as well as functions which are not supplied by standard software packages. The software is routinely applied in several clinical study groups.

PubMed ID: 22723912

Projects: GLA - German Lymphoma Alliance, Genetical Statistics and Systems Biology

Publication type: Not specified

Journal: PLoS One

Human Diseases: No Human Disease specified

Citation: PLoS One. 2012;7(6):e38960. doi: 10.1371/journal.pone.0038960. Epub 2012 Jun 15.

Date Published: 23rd Jun 2012

Registered Mode: by PubMed ID

Authors: A. Gross, M. Ziepert, M. Scholz

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Created: 17th Apr 2019 at 14:14

Last updated: 7th Dec 2021 at 17:58

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