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Comparing survival curves based on medians

BACKGROUND: Although some nonparametric methods have been proposed in the literature to test for the equality of median survival times for censored data in medical research, in general they have inflated type I error rates, which make their use limited in practice, especially when the sample sizes a...

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Autores principales: Chen, Zhongxue, Zhang, Guoyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4793497/
https://www.ncbi.nlm.nih.gov/pubmed/26983640
http://dx.doi.org/10.1186/s12874-016-0133-3
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author Chen, Zhongxue
Zhang, Guoyi
author_facet Chen, Zhongxue
Zhang, Guoyi
author_sort Chen, Zhongxue
collection PubMed
description BACKGROUND: Although some nonparametric methods have been proposed in the literature to test for the equality of median survival times for censored data in medical research, in general they have inflated type I error rates, which make their use limited in practice, especially when the sample sizes are small. METHODS: In this paper, we propose a new nonparametric test with a simple test statistic to compare median survival times. RESULTS: The results from our comprehensive simulation study show the new test controls type I error rate very well under all situations considered, even for small sample sizes. In addition, it has comparable detecting power compared to existing methods. Another advantage of the proposed test is its relatively simple formula that requires less computation. CONCLUSIONS: We propose a new statistical method for comparing survival curves based on their medians. The new method can be easily implemented and applied to censored event time data.
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spelling pubmed-47934972016-03-16 Comparing survival curves based on medians Chen, Zhongxue Zhang, Guoyi BMC Med Res Methodol Technical Advance BACKGROUND: Although some nonparametric methods have been proposed in the literature to test for the equality of median survival times for censored data in medical research, in general they have inflated type I error rates, which make their use limited in practice, especially when the sample sizes are small. METHODS: In this paper, we propose a new nonparametric test with a simple test statistic to compare median survival times. RESULTS: The results from our comprehensive simulation study show the new test controls type I error rate very well under all situations considered, even for small sample sizes. In addition, it has comparable detecting power compared to existing methods. Another advantage of the proposed test is its relatively simple formula that requires less computation. CONCLUSIONS: We propose a new statistical method for comparing survival curves based on their medians. The new method can be easily implemented and applied to censored event time data. BioMed Central 2016-03-16 /pmc/articles/PMC4793497/ /pubmed/26983640 http://dx.doi.org/10.1186/s12874-016-0133-3 Text en © Chen and Zhang. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Technical Advance
Chen, Zhongxue
Zhang, Guoyi
Comparing survival curves based on medians
title Comparing survival curves based on medians
title_full Comparing survival curves based on medians
title_fullStr Comparing survival curves based on medians
title_full_unstemmed Comparing survival curves based on medians
title_short Comparing survival curves based on medians
title_sort comparing survival curves based on medians
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4793497/
https://www.ncbi.nlm.nih.gov/pubmed/26983640
http://dx.doi.org/10.1186/s12874-016-0133-3
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