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评估在多中心试验中对二进制或连续结果的中央统计监测方法的性能:模拟研究
Li Ge1, Zhongkai Wang2, Charles C Liu2
1Gilead Sciences, Foster City 94404, CA, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison 53703, WI, USA.
Contemporary clinical trials
|May 25, 2024
概括
在临床试验中评估了中央统计监测 (CSM) 方法. 有限混合模型在检测数据异常方面表现优异,在识别潜在错误方面表现优于其他技术,从而提高了患者安全和数据完整性.
科学领域:
- 临床试验方法论 临床试验方法论
- 统计数据分析 统计数据分析
- 患者安全和数据完整性
背景情况:
- 质量研究监测对于患者安全和数据完整性至关重要.
- 监管机构和行业越来越多地主张基于风险的监测 (RBM) 和中央统计监测 (CSM).
- 评估用于识别多中心试验中不寻常数据模式的统计方法是很重要的.
研究的目的:
- 评估各种CSM技术的有效性,以确定多中心临床试验中不寻常的数据模式.
- 为了比较不同污染和过度分散场景下的不同统计方法的性能.
- 确定最合适的CSM方法,以确保数据完整性和患者安全.
主要方法:
- 模拟各种CSM技术,包括交叉测试,固定效果,混合效果和有限混合模型.
- 在不同样本大小,污染率和过度分散的场景中进行评估.
- 使用与值无关的指标,如曲线下的面积 (AUC) 和平均精度 (AP),用于绩效评估.
主要成果:
- 适应性有限混合模型在AUC和AP方面表现出优异的性能,特别是在30%的污染下.
- 混合效应模型在较低的污染率下表现良好,但在偏差较高的情况下表现下降.
- 交叉测试和固定效应方法表现不佳,特别是偏差增加,突出了检测系统错误的局限性.
结论:
- 仅仅依靠灵敏度和特异性是不足以衡量CSM中的预测性能.
- 有限混合方法通过减轻异常影响来提供一致的性能,使其成为一个强大的选择.
- 假阳性/假阴性的研究具体成本和可用的监测资源应该指导CSM的实际实施.
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