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

Overview of attention for article published in Trials, July 2016
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Title
Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application
Published in
Trials, July 2016
DOI 10.1186/s13063-016-1457-3
Pubmed ID
Authors

Yu Jiang, Peter Guarino, Shuangge Ma, Steve Simon, Matthew S. Mayo, Rama Raghavan, Byron J. Gajewski

Abstract

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. 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. 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. This developed accrual software provides a more convenient platform for estimation and prediction of the accrual process.

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The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Unknown 75 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 13%
Student > Ph. D. Student 10 13%
Student > Bachelor 9 12%
Student > Master 8 11%
Student > Doctoral Student 4 5%
Other 9 12%
Unknown 26 34%
Readers by discipline Count As %
Medicine and Dentistry 14 18%
Nursing and Health Professions 7 9%
Psychology 6 8%
Computer Science 4 5%
Social Sciences 3 4%
Other 10 13%
Unknown 32 42%