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Comparative optimism in models involving both classical clinical and gene expression information
BACKGROUND: In cancer research, most clinical variables have already been investigated and are now well established. The use of transcriptomic variables has raised two problems: restricting their number and validating their significance. Thus, their contribution to prognosis is currently thought to...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2008
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2588601/ https://www.ncbi.nlm.nih.gov/pubmed/18854055 http://dx.doi.org/10.1186/1471-2105-9-434 |
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author | Truntzer, Caroline Maucort-Boulch, Delphine Roy, Pascal |
author_facet | Truntzer, Caroline Maucort-Boulch, Delphine Roy, Pascal |
author_sort | Truntzer, Caroline |
collection | PubMed |
description | BACKGROUND: In cancer research, most clinical variables have already been investigated and are now well established. The use of transcriptomic variables has raised two problems: restricting their number and validating their significance. Thus, their contribution to prognosis is currently thought to be overestimated. The aim of this study was to determine to what extent optimism concerning current transcriptomic models may lead to overestimation of the contribution of transcriptomic variables to survival prognosis. RESULTS: To achieve this goal, Cox proportional hazards models that adjust for clinical and transcriptomic variables were built. As the relevance of the clinical variables had already been established, they were not submitted to selection. As for genes, they were selected using both univariate and multivariate methods. Optimism and the contribution of clinical and transcriptomic variables to prognosis were compared through simulations and by using the Kent and O'Quigley ρ(2 )measure of dependence. We showed that the optimism relative to clinical variables was low because these are no longer submitted to selection of relevant variables. In contrast, for genes, the selection process introduced high optimism, which increased when the proportion of genes of interest decreased. However, this optimism can be decreased by increasing the number of samples. CONCLUSION: Two phenomena have to be taken into account by comparing the predictive power and optimism of clinical variables and those of genes: overestimation for genes due to the selection process and underestimation for clinical variables due to the omission of relevant genes. In comparison with genes, the predictive value of validated clinical variables is not overestimated, which should be kept in mind in future studies involving both clinical and transcriptomic variables. |
format | Text |
id | pubmed-2588601 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-25886012008-11-28 Comparative optimism in models involving both classical clinical and gene expression information Truntzer, Caroline Maucort-Boulch, Delphine Roy, Pascal BMC Bioinformatics Research Article BACKGROUND: In cancer research, most clinical variables have already been investigated and are now well established. The use of transcriptomic variables has raised two problems: restricting their number and validating their significance. Thus, their contribution to prognosis is currently thought to be overestimated. The aim of this study was to determine to what extent optimism concerning current transcriptomic models may lead to overestimation of the contribution of transcriptomic variables to survival prognosis. RESULTS: To achieve this goal, Cox proportional hazards models that adjust for clinical and transcriptomic variables were built. As the relevance of the clinical variables had already been established, they were not submitted to selection. As for genes, they were selected using both univariate and multivariate methods. Optimism and the contribution of clinical and transcriptomic variables to prognosis were compared through simulations and by using the Kent and O'Quigley ρ(2 )measure of dependence. We showed that the optimism relative to clinical variables was low because these are no longer submitted to selection of relevant variables. In contrast, for genes, the selection process introduced high optimism, which increased when the proportion of genes of interest decreased. However, this optimism can be decreased by increasing the number of samples. CONCLUSION: Two phenomena have to be taken into account by comparing the predictive power and optimism of clinical variables and those of genes: overestimation for genes due to the selection process and underestimation for clinical variables due to the omission of relevant genes. In comparison with genes, the predictive value of validated clinical variables is not overestimated, which should be kept in mind in future studies involving both clinical and transcriptomic variables. BioMed Central 2008-10-15 /pmc/articles/PMC2588601/ /pubmed/18854055 http://dx.doi.org/10.1186/1471-2105-9-434 Text en Copyright © 2008 Truntzer et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Truntzer, Caroline Maucort-Boulch, Delphine Roy, Pascal Comparative optimism in models involving both classical clinical and gene expression information |
title | Comparative optimism in models involving both classical clinical and gene expression information |
title_full | Comparative optimism in models involving both classical clinical and gene expression information |
title_fullStr | Comparative optimism in models involving both classical clinical and gene expression information |
title_full_unstemmed | Comparative optimism in models involving both classical clinical and gene expression information |
title_short | Comparative optimism in models involving both classical clinical and gene expression information |
title_sort | comparative optimism in models involving both classical clinical and gene expression information |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2588601/ https://www.ncbi.nlm.nih.gov/pubmed/18854055 http://dx.doi.org/10.1186/1471-2105-9-434 |
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