ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts

dc.contributor.authorLaajala TD
dc.contributor.authorMurtojärvi M
dc.contributor.authorVirkki A
dc.contributor.authorAittokallio T
dc.contributor.organizationfi=matematiikka|en=Mathematics|
dc.contributor.organizationfi=sovellettu matematiikka|en=Applied mathematics|
dc.contributor.organizationfi=tietojenkäsittelytiede|en=Computer Science|
dc.contributor.organizationfi=tyks, vsshp|en=tyks, varha|
dc.contributor.organization-code1.2.246.10.2458963.20.23479734818
dc.contributor.organization-code1.2.246.10.2458963.20.41687507875
dc.contributor.organization-code1.2.246.10.2458963.20.48078768388
dc.converis.publication-id32107871
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/32107871
dc.date.accessioned2022-10-28T14:15:17Z
dc.date.available2022-10-28T14:15:17Z
dc.description.abstract<h4>Motivation: </h4><p>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.</p><h4>Results: </h4><p>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.</p><h4>Availability: </h4><p>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.</p><h4>Supplementary information: </h4><p>Supplementary data are available at Bioinformatics online.<br /></p>
dc.format.pagerange3957
dc.format.pagerange3959
dc.identifier.eissn1367-4811
dc.identifier.jour-issn1367-4803
dc.identifier.olddbid187198
dc.identifier.oldhandle10024/170292
dc.identifier.urihttps://www.utupub.fi/handle/11111/42687
dc.identifier.urlhttps://academic.oup.com/bioinformatics/article/34/22/3957/5038459
dc.identifier.urnURN:NBN:fi-fe2021042719375
dc.language.isoen
dc.okm.affiliatedauthorLaajala, Daniel
dc.okm.affiliatedauthorMurtojärvi, Mika
dc.okm.affiliatedauthorAittokallio, Tero
dc.okm.affiliatedauthorDataimport, tyks, vsshp
dc.okm.discipline112 Statistics and probabilityen_GB
dc.okm.discipline113 Computer and information sciencesen_GB
dc.okm.discipline3122 Cancersen_GB
dc.okm.discipline112 Tilastotiedefi_FI
dc.okm.discipline113 Tietojenkäsittely ja informaatiotieteetfi_FI
dc.okm.discipline3122 Syöpätauditfi_FI
dc.okm.internationalcopublicationnot an international co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA1 ScientificArticle
dc.publisherOxford University Press
dc.publisher.countryUnited Kingdomen_GB
dc.publisher.countryBritanniafi_FI
dc.publisher.country-codeGB
dc.relation.doi10.1093/bioinformatics/bty477
dc.relation.ispartofjournalBioinformatics
dc.relation.issue22
dc.relation.volume34
dc.source.identifierhttps://www.utupub.fi/handle/10024/170292
dc.titleePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts
dc.year.issued2018

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