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Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application
BACKGROUND: Subject recruitment for medical research is challenging. Slow patient accrual leads to increased costs and delays in treatment advances. Researchers need reliable tools to manage and predict the accrual rate. The previously developed Bayesian method integrates researchers’ experience on...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4957321/ https://www.ncbi.nlm.nih.gov/pubmed/27449769 http://dx.doi.org/10.1186/s13063-016-1457-3 |
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author | Jiang, Yu Guarino, Peter Ma, Shuangge Simon, Steve Mayo, Matthew S. Raghavan, Rama Gajewski, Byron J. |
author_facet | Jiang, Yu Guarino, Peter Ma, Shuangge Simon, Steve Mayo, Matthew S. Raghavan, Rama Gajewski, Byron J. |
author_sort | Jiang, Yu |
collection | PubMed |
description | BACKGROUND: Subject recruitment for medical research is challenging. Slow patient accrual leads to increased costs and delays in treatment advances. Researchers need reliable tools to manage and predict the accrual rate. The previously developed Bayesian method integrates researchers’ experience on former trials and data from an ongoing study, providing a reliable prediction of accrual rate for clinical studies. METHODS: In this paper, we present a user-friendly graphical user interface program developed in R. A closed-form solution for the total subjects that can be recruited within a fixed time is derived. We also present a built-in Android system using Java for web browsers and mobile devices. RESULTS: Using the accrual software, we re-evaluated the Veteran Affairs Cooperative Studies Program 558— ROBOTICS study. The application of the software in monitoring and management of recruitment is illustrated for different stages of the trial. CONCLUSIONS: This developed accrual software provides a more convenient platform for estimation and prediction of the accrual process. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13063-016-1457-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4957321 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-49573212016-07-23 Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application Jiang, Yu Guarino, Peter Ma, Shuangge Simon, Steve Mayo, Matthew S. Raghavan, Rama Gajewski, Byron J. Trials Research BACKGROUND: Subject recruitment for medical research is challenging. Slow patient accrual leads to increased costs and delays in treatment advances. Researchers need reliable tools to manage and predict the accrual rate. The previously developed Bayesian method integrates researchers’ experience on former trials and data from an ongoing study, providing a reliable prediction of accrual rate for clinical studies. METHODS: In this paper, we present a user-friendly graphical user interface program developed in R. A closed-form solution for the total subjects that can be recruited within a fixed time is derived. We also present a built-in Android system using Java for web browsers and mobile devices. RESULTS: Using the accrual software, we re-evaluated the Veteran Affairs Cooperative Studies Program 558— ROBOTICS study. The application of the software in monitoring and management of recruitment is illustrated for different stages of the trial. CONCLUSIONS: This developed accrual software provides a more convenient platform for estimation and prediction of the accrual process. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13063-016-1457-3) contains supplementary material, which is available to authorized users. BioMed Central 2016-07-22 /pmc/articles/PMC4957321/ /pubmed/27449769 http://dx.doi.org/10.1186/s13063-016-1457-3 Text en © Jiang et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Jiang, Yu Guarino, Peter Ma, Shuangge Simon, Steve Mayo, Matthew S. Raghavan, Rama Gajewski, Byron J. Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application |
title | Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application |
title_full | Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application |
title_fullStr | Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application |
title_full_unstemmed | Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application |
title_short | Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application |
title_sort | bayesian accrual prediction for interim review of clinical studies: open source r package and smartphone application |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4957321/ https://www.ncbi.nlm.nih.gov/pubmed/27449769 http://dx.doi.org/10.1186/s13063-016-1457-3 |
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