CluePoints S.A., Avenue Albert Einstein, 2a, 1348, Louvain-la-Neuve, Belgium.
Statistical Consultant, Ingelheim Am Rhein, Germany.
Ther Innov Regul Sci. 2022 Jan;56(1):130-136. doi: 10.1007/s43441-021-00341-5. Epub 2021 Sep 29.
A central statistical assessment of the quality of data collected in clinical trials can improve the quality and efficiency of sponsor oversight of clinical investigations.
The database of a large randomized clinical trial with known fraud was reanalyzed with a view to identifying, using only statistical monitoring techniques, the center where fraud had been confirmed. The analysis was conducted with an unsupervised statistical monitoring software using mixed-effects statistical models. The statistical analyst was unaware of the location, nature, and extent of the fraud.
Five centers were detected as atypical, including the center with known fraud (which was ranked 2). An incremental analysis showed that the center with known fraud could have been detected after only 25% of its data had been reported.
An unsupervised approach to central monitoring, using mixed-effects statistical models, is effective at detecting centers with fraud or other data anomalies in clinical trials.
对临床试验中收集的数据进行集中统计评估可以提高赞助商对临床研究监督的质量和效率。
对一个已知存在欺诈的大型随机临床试验数据库进行重新分析，目的是仅使用统计监测技术确定已确认欺诈的中心。该分析使用无监督统计监测软件和混合效应统计模型进行。统计分析师不知道欺诈的位置、性质和程度。
发现五个中心是异常的，包括已知欺诈的中心（排名第二）。增量分析表明，在仅报告了其 25%的数据后，就可以检测到已知欺诈的中心。
使用混合效应统计模型进行无监督的集中监测方法可有效检测临床试验中存在欺诈或其他数据异常的中心。
