Title |
Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application
|
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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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 6 | 38% |
United Kingdom | 2 | 13% |
Belgium | 1 | 6% |
Italy | 1 | 6% |
Brazil | 1 | 6% |
Chile | 1 | 6% |
Unknown | 4 | 25% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 12 | 75% |
Scientists | 2 | 13% |
Practitioners (doctors, other healthcare professionals) | 2 | 13% |
Mendeley readers
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% |