A1 Refereed original research article in a scientific journal
ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts
Authors: Laajala TD, Murtojärvi M, Virkki A, Aittokallio T
Publisher: Oxford University Press
Publication year: 2018
Journal: Bioinformatics
Volume: 34
Issue: 22
First page : 3957
Last page: 3959
Number of pages: 3
ISSN: 1367-4803
eISSN: 1367-4811
DOI: https://doi.org/10.1093/bioinformatics/bty477
Web address : https://academic.oup.com/bioinformatics/article/34/22/3957/5038459
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/32107871
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: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.
Downloadable publication This is an electronic reprint of the original article. |  |
