Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application

Abstract 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 researchersâ 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 558â 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.

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PID https://www.doi.org/10.6084/m9.figshare.c.3623240
PID https://www.doi.org/10.6084/m9.figshare.c.3623240.v1
URL http://dx.doi.org/10.6084/m9.figshare.c.3623240
URL http://dx.doi.org/10.6084/m9.figshare.c.3623240.v1
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Author Jiang, Yu
Author Guarino, Peter
Author Shuangge Ma
Author Simon, Steve
Author Mayo, Matthew
Author Raghavan, Rama
Author Gajewski, Byron
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Collected From Datacite
Hosted By figshare
Publication Date 2016-01-01
Publisher Figshare
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Language UNKNOWN
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keyword FOS: Biological sciences
keyword FOS: Computer and information sciences
keyword mesheuropmc.digestive, oral, and skin physiology
keyword FOS: Health sciences
system:type other
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Source https://science-innovation-policy.openaire.eu/search/other?orpId=dedup_wf_001::fbde1c54fde4191b62767576ec299d69
Author jsonws_user
Last Updated 20 December 2020, 03:30 (CET)
Created 20 December 2020, 03:30 (CET)