临床研究数据处理方法的错误率:通过PubMed识别的手稿的系统审查和元分析
Maryam Y Garza1, Tremaine Williams2, Songthip Ounpraseuth3
1Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, the United States of America; University of Texas Health Science Center at San Antonio, Joe R. & Teresa Lozano Long School of Medicine, San Antonio, TX, the United States of America.
数据处理方法显著影响临床试验的准确性. 双重数据输入显示了最低的错误率,而手动记录抽象 (MRA) 具有很高的可变性,可能会影响试验的有效性和功率.
科学领域:
- 临床研究方法论临床研究方法论
- 临床试验中的数据管理
- 生物统计学 生物统计学
背景情况:
- 预防数据错误对于临床试验的可重复性,安全性和有效性至关重要.
- 对数据准确性的经验评估存在,但缺乏系统的综合.
- 关于不同数据处理方法相对准确性的证据有限.
研究的目的:
- 在临床试验中使用的各种数据处理方法中,系统地审查和综合有关数据准确性的经验证据.
- 为了比较与不同数据处理技术相关的错误率.
主要方法:
- 对PubMed识别的评估临床试验数据处理方法数据质量的研究进行系统性文献综述.
- 数据准确性信息的定量抽象.
- 使用弗里曼-图基变换和通用线性混合模型来估计聚合错误率的单一比例的元分析.
主要成果:
- 对93篇论文 (1978-2008) 的分析揭示了数据处理准确度的巨大差异,每万个字段的错误率从2到2784不等.
- 手动记录抽象 (MRA) 显示了高的聚合错误率 (6.57%) 和显著的变化.
- 光学扫描,单次数据输入和双次数据输入的聚合错误率分别为0.74%,0.29%和0.14%.
结论:
- 数据处理方法是临床研究中数据准确度变化的重要因素.
- 像MRA这样的方法的高错误率可能会影响试验决策,并需要更大的样本大小.
- 数据处理方法的选择极大地影响了过程能力和临床试验结果的整体有效性.
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