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Cancer Survival Data Representation for Improved Parametric and Dynamic Lifetime Analysis
Survival functions are often characterized by a median survival time or a 5-year survival. Whether or not such representation is sufficient depends on tumour development. Different tumour stages have different mean survival times after therapy. The validity of an exponential decay and the origins of...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6955760/ https://www.ncbi.nlm.nih.gov/pubmed/31661787 http://dx.doi.org/10.3390/healthcare7040123 |
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author | Vandamme, Lode K.J. Wouters, Peter A.A.F. Slooter, Gerrit D. de Hingh, Ignace H.J.T. |
author_facet | Vandamme, Lode K.J. Wouters, Peter A.A.F. Slooter, Gerrit D. de Hingh, Ignace H.J.T. |
author_sort | Vandamme, Lode K.J. |
collection | PubMed |
description | Survival functions are often characterized by a median survival time or a 5-year survival. Whether or not such representation is sufficient depends on tumour development. Different tumour stages have different mean survival times after therapy. The validity of an exponential decay and the origins of deviations are substantiated. The paper shows, that representation of survival data as logarithmic functions visualizes differences better, which allows for differentiating short- and long-term dynamic lifetime. It is more instructive to represent the changing lifetime expectancy for an individual who has survived a certain time, which can be significantly different from the initial expectation just after treatment. Survival data from 15 publications on cancer are compared and re-analysed based on the well-established: (i) exponential decay (ii) piecewise constant hazard (iii) Weibull model and our proposed parametric survival models, (iv) the two-τ and (v) the sliding-τ model. The new models describe either accelerated aging or filtering out of defects with numerical parameters with a physical meaning and add information to the usually provided log-rank P-value or median survival. The statistical inhomogeneity in a group by mixing up different tumour stages, metastases and treatments is the main origin for deviations from the exponential decay. |
format | Online Article Text |
id | pubmed-6955760 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-69557602020-01-23 Cancer Survival Data Representation for Improved Parametric and Dynamic Lifetime Analysis Vandamme, Lode K.J. Wouters, Peter A.A.F. Slooter, Gerrit D. de Hingh, Ignace H.J.T. Healthcare (Basel) Article Survival functions are often characterized by a median survival time or a 5-year survival. Whether or not such representation is sufficient depends on tumour development. Different tumour stages have different mean survival times after therapy. The validity of an exponential decay and the origins of deviations are substantiated. The paper shows, that representation of survival data as logarithmic functions visualizes differences better, which allows for differentiating short- and long-term dynamic lifetime. It is more instructive to represent the changing lifetime expectancy for an individual who has survived a certain time, which can be significantly different from the initial expectation just after treatment. Survival data from 15 publications on cancer are compared and re-analysed based on the well-established: (i) exponential decay (ii) piecewise constant hazard (iii) Weibull model and our proposed parametric survival models, (iv) the two-τ and (v) the sliding-τ model. The new models describe either accelerated aging or filtering out of defects with numerical parameters with a physical meaning and add information to the usually provided log-rank P-value or median survival. The statistical inhomogeneity in a group by mixing up different tumour stages, metastases and treatments is the main origin for deviations from the exponential decay. MDPI 2019-10-28 /pmc/articles/PMC6955760/ /pubmed/31661787 http://dx.doi.org/10.3390/healthcare7040123 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Vandamme, Lode K.J. Wouters, Peter A.A.F. Slooter, Gerrit D. de Hingh, Ignace H.J.T. Cancer Survival Data Representation for Improved Parametric and Dynamic Lifetime Analysis |
title | Cancer Survival Data Representation for Improved Parametric and Dynamic Lifetime Analysis |
title_full | Cancer Survival Data Representation for Improved Parametric and Dynamic Lifetime Analysis |
title_fullStr | Cancer Survival Data Representation for Improved Parametric and Dynamic Lifetime Analysis |
title_full_unstemmed | Cancer Survival Data Representation for Improved Parametric and Dynamic Lifetime Analysis |
title_short | Cancer Survival Data Representation for Improved Parametric and Dynamic Lifetime Analysis |
title_sort | cancer survival data representation for improved parametric and dynamic lifetime analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6955760/ https://www.ncbi.nlm.nih.gov/pubmed/31661787 http://dx.doi.org/10.3390/healthcare7040123 |
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