临床研究中期审查的贝叶斯累积预测：开源R包和智能手机应用程序。
Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application.
作者信息
Jiang Yu, Guarino Peter, Ma Shuangge, Simon Steve, Mayo Matthew S, Raghavan Rama, Gajewski Byron J
机构信息
Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, Memphis, TN, 38152, USA.
Cooperative Studies Program, VA Connecticut Healthcare System, West Haven, CT, 06516, USA.
出版信息
Trials. 2016 Jul 22;17(1):336. doi: 10.1186/s13063-016-1457-3.
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.
背景
医学研究中的受试者招募具有挑战性。患者入组缓慢会导致成本增加和治疗进展延迟。研究人员需要可靠的工具来管理和预测入组率。先前开发的贝叶斯方法整合了研究人员对以前试验的经验和来自正在进行的研究的数据，为临床研究的入组率提供了可靠的预测。
方法
在本文中，我们展示了一个用R语言开发的用户友好型图形用户界面程序。推导了在固定时间内可招募的总受试者的闭式解。我们还展示了一个使用Java为网页浏览器和移动设备构建的安卓系统。
结果
使用入组软件，我们重新评估了退伍军人事务部合作研究项目558 - 机器人研究。该软件在试验不同阶段用于监测和管理招募的应用得到了说明。
结论
这种开发的入组软件为入组过程的估计和预测提供了一个更便捷的平台。
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