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基于网络的数据管理用于肌萎缩侧索硬化症的一项II期临床试验。

Web-based data management for a phase II clinical trial in ALS.

作者信息

Buchsbaum Richard, Kaufmann Petra, Barsdorf Alexandra I, Arbing Rachel, Montes Jacqueline, Thompson John L P

机构信息

Department of Biostatistics, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032, USA.

出版信息

Amyotroph Lateral Scler. 2009 Oct-Dec;10(5-6):374-7. doi: 10.3109/17482960802378998.

Abstract

The objective was to report on the creation, features and performance of a web-based data management system for a two-stage phase II randomized clinical trial of Co-Enzyme Q10 in ALS. We created a relatively comprehensive web-based data system that provided electronic data entry; patient management utilities; adverse event reporting, safety monitoring, and invoice generation; and standardized coding for medications and adverse events. In stage 1, clinical sites submitted 7207 forms reporting on 105 patients followed for 10 months. Less than 0.7% of submitted forms contained errors. At the time of the delivery of the analysis data set, only four errors remained unresolved. Data were available quickly, with a median time from event to data posting of two days. The data set was locked and the analysis data set produced nine days after the final patient visit. A survey of trial personnel yielded generally positive feedback, with 75% of respondents wishing to use a similar system in the future. Given sufficient resources, a comprehensive web-based data management system can meet the need for clean, available data in clinical trials in ALS and similar diseases, and can contribute significantly to their efficient execution.

摘要

目的是报告用于肌萎缩侧索硬化症(ALS)辅酶Q10两阶段II期随机临床试验的基于网络的数据管理系统的创建、功能和性能。我们创建了一个相对全面的基于网络的数据系统,该系统提供电子数据录入;患者管理实用程序;不良事件报告、安全监测和发票生成;以及药物和不良事件的标准化编码。在第1阶段,临床站点提交了7207份表格,报告了105名患者随访10个月的情况。提交的表格中错误率不到0.7%。在交付分析数据集时,只有四个错误仍未解决。数据很快就能获取,从事件发生到数据发布的中位时间为两天。数据集被锁定,分析数据集在最后一名患者访视九天后生成。对试验人员的一项调查得到了总体积极的反馈,75%的受访者希望未来使用类似系统。如果有足够的资源,一个全面的基于网络的数据管理系统可以满足ALS及类似疾病临床试验中对干净、可用数据的需求,并能为高效开展此类试验做出重大贡献。

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