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Flexible modeling improves assessment of prognostic value of C-reactive protein in advanced non-small cell lung cancer

BACKGROUND: C-reactive protein (CRP) is gaining credibility as a prognostic factor in different cancers. Cox's proportional hazard (PH) model is usually used to assess prognostic factors. However, this model imposes a priori assumptions, which are rarely tested, that (1) the hazard ratio associ...

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Autores principales: Gagnon, B, Abrahamowicz, M, Xiao, Y, Beauchamp, M-E, MacDonald, N, Kasymjanova, G, Kreisman, H, Small, D
Formato: Texto
Lenguaje:English
Publicado: Nature Publishing Group 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2853092/
https://www.ncbi.nlm.nih.gov/pubmed/20234363
http://dx.doi.org/10.1038/sj.bjc.6605603
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author Gagnon, B
Abrahamowicz, M
Xiao, Y
Beauchamp, M-E
MacDonald, N
Kasymjanova, G
Kreisman, H
Small, D
author_facet Gagnon, B
Abrahamowicz, M
Xiao, Y
Beauchamp, M-E
MacDonald, N
Kasymjanova, G
Kreisman, H
Small, D
author_sort Gagnon, B
collection PubMed
description BACKGROUND: C-reactive protein (CRP) is gaining credibility as a prognostic factor in different cancers. Cox's proportional hazard (PH) model is usually used to assess prognostic factors. However, this model imposes a priori assumptions, which are rarely tested, that (1) the hazard ratio associated with each prognostic factor remains constant across the follow-up (PH assumption) and (2) the relationship between a continuous predictor and the logarithm of the mortality hazard is linear (linearity assumption). METHODS: We tested these two assumptions of the Cox's PH model for CRP, using a flexible statistical model, while adjusting for other known prognostic factors, in a cohort of 269 patients newly diagnosed with non-small cell lung cancer (NSCLC). RESULTS: In the Cox's PH model, high CRP increased the risk of death (HR=1.11 per each doubling of CRP value, 95% CI: 1.03–1.20, P=0.008). However, both the PH assumption (P=0.033) and the linearity assumption (P=0.015) were rejected for CRP, measured at the initiation of chemotherapy, which kept its prognostic value for approximately 18 months. CONCLUSION: Our analysis shows that flexible modeling provides new insights regarding the value of CRP as a prognostic factor in NSCLC and that Cox's PH model underestimates early risks associated with high CRP.
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spelling pubmed-28530922011-03-30 Flexible modeling improves assessment of prognostic value of C-reactive protein in advanced non-small cell lung cancer Gagnon, B Abrahamowicz, M Xiao, Y Beauchamp, M-E MacDonald, N Kasymjanova, G Kreisman, H Small, D Br J Cancer Clinical Study BACKGROUND: C-reactive protein (CRP) is gaining credibility as a prognostic factor in different cancers. Cox's proportional hazard (PH) model is usually used to assess prognostic factors. However, this model imposes a priori assumptions, which are rarely tested, that (1) the hazard ratio associated with each prognostic factor remains constant across the follow-up (PH assumption) and (2) the relationship between a continuous predictor and the logarithm of the mortality hazard is linear (linearity assumption). METHODS: We tested these two assumptions of the Cox's PH model for CRP, using a flexible statistical model, while adjusting for other known prognostic factors, in a cohort of 269 patients newly diagnosed with non-small cell lung cancer (NSCLC). RESULTS: In the Cox's PH model, high CRP increased the risk of death (HR=1.11 per each doubling of CRP value, 95% CI: 1.03–1.20, P=0.008). However, both the PH assumption (P=0.033) and the linearity assumption (P=0.015) were rejected for CRP, measured at the initiation of chemotherapy, which kept its prognostic value for approximately 18 months. CONCLUSION: Our analysis shows that flexible modeling provides new insights regarding the value of CRP as a prognostic factor in NSCLC and that Cox's PH model underestimates early risks associated with high CRP. Nature Publishing Group 2010-03-30 2010-03-16 /pmc/articles/PMC2853092/ /pubmed/20234363 http://dx.doi.org/10.1038/sj.bjc.6605603 Text en Copyright © 2010 Cancer Research UK https://creativecommons.org/licenses/by/4.0/This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material.If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/.
spellingShingle Clinical Study
Gagnon, B
Abrahamowicz, M
Xiao, Y
Beauchamp, M-E
MacDonald, N
Kasymjanova, G
Kreisman, H
Small, D
Flexible modeling improves assessment of prognostic value of C-reactive protein in advanced non-small cell lung cancer
title Flexible modeling improves assessment of prognostic value of C-reactive protein in advanced non-small cell lung cancer
title_full Flexible modeling improves assessment of prognostic value of C-reactive protein in advanced non-small cell lung cancer
title_fullStr Flexible modeling improves assessment of prognostic value of C-reactive protein in advanced non-small cell lung cancer
title_full_unstemmed Flexible modeling improves assessment of prognostic value of C-reactive protein in advanced non-small cell lung cancer
title_short Flexible modeling improves assessment of prognostic value of C-reactive protein in advanced non-small cell lung cancer
title_sort flexible modeling improves assessment of prognostic value of c-reactive protein in advanced non-small cell lung cancer
topic Clinical Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2853092/
https://www.ncbi.nlm.nih.gov/pubmed/20234363
http://dx.doi.org/10.1038/sj.bjc.6605603
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