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ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts
MOTIVATION: Prognostic models are widely used in clinical decision-making, such as risk stratification and tailoring treatment strategies, with the aim to improve patient outcomes while reducing overall healthcare costs. While prognostic models have been adopted into clinical use, benchmarking their...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223370/ https://www.ncbi.nlm.nih.gov/pubmed/29912284 http://dx.doi.org/10.1093/bioinformatics/bty477 |
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author | Laajala, Teemu D Murtojärvi, Mika Virkki, Arho Aittokallio, Tero |
author_facet | Laajala, Teemu D Murtojärvi, Mika Virkki, Arho Aittokallio, Tero |
author_sort | Laajala, Teemu D |
collection | PubMed |
description | MOTIVATION: Prognostic models are widely used in clinical decision-making, such as risk stratification and tailoring treatment strategies, with the aim to improve patient outcomes while reducing overall healthcare costs. While prognostic models have been adopted into clinical use, benchmarking their performance has been difficult due to lack of open clinical datasets. The recent DREAM 9.5 Prostate Cancer Challenge carried out an extensive benchmarking of prognostic models for metastatic Castration-Resistant Prostate Cancer (mCRPC), based on multiple cohorts of open clinical trial data. RESULTS: We make available an open-source implementation of the top-performing model, ePCR, along with an extended toolbox for its further re-use and development, and demonstrate how to best apply the implemented model to real-world data cohorts of advanced prostate cancer patients. AVAILABILITY AND IMPLEMENTATION: The open-source R-package ePCR and its reference documentation are available at the Central R Archive Network (CRAN): https://CRAN.R-project.org/package=ePCR. R-vignette provides step-by-step examples for the ePCR usage. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-6223370 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-62233702018-11-14 ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts Laajala, Teemu D Murtojärvi, Mika Virkki, Arho Aittokallio, Tero Bioinformatics Applications Notes MOTIVATION: Prognostic models are widely used in clinical decision-making, such as risk stratification and tailoring treatment strategies, with the aim to improve patient outcomes while reducing overall healthcare costs. While prognostic models have been adopted into clinical use, benchmarking their performance has been difficult due to lack of open clinical datasets. The recent DREAM 9.5 Prostate Cancer Challenge carried out an extensive benchmarking of prognostic models for metastatic Castration-Resistant Prostate Cancer (mCRPC), based on multiple cohorts of open clinical trial data. RESULTS: We make available an open-source implementation of the top-performing model, ePCR, along with an extended toolbox for its further re-use and development, and demonstrate how to best apply the implemented model to real-world data cohorts of advanced prostate cancer patients. AVAILABILITY AND IMPLEMENTATION: The open-source R-package ePCR and its reference documentation are available at the Central R Archive Network (CRAN): https://CRAN.R-project.org/package=ePCR. R-vignette provides step-by-step examples for the ePCR usage. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2018-11-15 2018-06-15 /pmc/articles/PMC6223370/ /pubmed/29912284 http://dx.doi.org/10.1093/bioinformatics/bty477 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Applications Notes Laajala, Teemu D Murtojärvi, Mika Virkki, Arho Aittokallio, Tero ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts |
title | ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts |
title_full | ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts |
title_fullStr | ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts |
title_full_unstemmed | ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts |
title_short | ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts |
title_sort | epcr: an r-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223370/ https://www.ncbi.nlm.nih.gov/pubmed/29912284 http://dx.doi.org/10.1093/bioinformatics/bty477 |
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