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Visualizing the quantile survival time difference curve

The difference between the pth quantiles of 2 survival functions can be used to compare patients' survival between 2 therapies. Setting p = 0.5 yields the median survival time difference. Varying p between 0 and 1 defines the quantile survival time difference curve which can be straightforwardl...

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Detalles Bibliográficos
Autores principales: Heinzl, Harald, Mittlboeck, Martina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6099283/
https://www.ncbi.nlm.nih.gov/pubmed/29790230
http://dx.doi.org/10.1111/jep.12948
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author Heinzl, Harald
Mittlboeck, Martina
author_facet Heinzl, Harald
Mittlboeck, Martina
author_sort Heinzl, Harald
collection PubMed
description The difference between the pth quantiles of 2 survival functions can be used to compare patients' survival between 2 therapies. Setting p = 0.5 yields the median survival time difference. Varying p between 0 and 1 defines the quantile survival time difference curve which can be straightforwardly estimated by the horizontal differences between 2 Kaplan‐Meier curves. The estimate's variability can be visualized by adding either a bundle of resampled bootstrap step functions or, alternatively, approximate bootstrap confidence bands. The user‐friendly SAS software macro %kmdiff enables the straightforward application of this exploratory graphical approach. The macro is described, and its application is exemplified with breast cancer data. The advantages and limitations of the approach are discussed.
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spelling pubmed-60992832018-08-23 Visualizing the quantile survival time difference curve Heinzl, Harald Mittlboeck, Martina J Eval Clin Pract Original Articles The difference between the pth quantiles of 2 survival functions can be used to compare patients' survival between 2 therapies. Setting p = 0.5 yields the median survival time difference. Varying p between 0 and 1 defines the quantile survival time difference curve which can be straightforwardly estimated by the horizontal differences between 2 Kaplan‐Meier curves. The estimate's variability can be visualized by adding either a bundle of resampled bootstrap step functions or, alternatively, approximate bootstrap confidence bands. The user‐friendly SAS software macro %kmdiff enables the straightforward application of this exploratory graphical approach. The macro is described, and its application is exemplified with breast cancer data. The advantages and limitations of the approach are discussed. John Wiley and Sons Inc. 2018-05-23 2018-08 /pmc/articles/PMC6099283/ /pubmed/29790230 http://dx.doi.org/10.1111/jep.12948 Text en © 2018 The Authors Journal of Evaluation in Clinical Practice Published by John Wiley & Sons Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Heinzl, Harald
Mittlboeck, Martina
Visualizing the quantile survival time difference curve
title Visualizing the quantile survival time difference curve
title_full Visualizing the quantile survival time difference curve
title_fullStr Visualizing the quantile survival time difference curve
title_full_unstemmed Visualizing the quantile survival time difference curve
title_short Visualizing the quantile survival time difference curve
title_sort visualizing the quantile survival time difference curve
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6099283/
https://www.ncbi.nlm.nih.gov/pubmed/29790230
http://dx.doi.org/10.1111/jep.12948
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