aThe EMMES Corporation, Rockville, MD, USA.
Clin Trials. 2014 Apr;11(2):205-17. doi: 10.1177/1740774513508028. Epub 2013 Dec 2.
Site monitoring and source document verification account for 15%-30% of clinical trial costs. An alternative is to streamline site monitoring to focus on correcting trial-specific risks identified by central data monitoring. This risk-based approach could preserve or even improve the quality of clinical trial data and human subject protection compared to site monitoring focused primarily on source document verification.
To determine whether a central review by statisticians using data submitted to the Food and Drug Administration (FDA) by clinical trial sponsors can identify problem sites and trials that failed FDA site inspections.
An independent Analysis Center (AC) analyzed data from four anonymous new drug applications (NDAs) where FDA had performed site inspections overseen by FDA's Office of Scientific Investigations (OSI). FDA team members in the OSI chose the four NDAs from among all NDAs with data in Study Data Tabulation Model (SDTM) format. Two of the NDAs had data that OSI had deemed unreliable in support of the application after FDA site inspections identified serious data integrity problems. The other two NDAs had clinical data that OSI deemed reliable after site inspections. At the outset, the AC knew only that the experimental design specified two NDAs with significant problems. FDA gave the AC no information about which NDAs had problems, how many sites were inspected, or how many were found to have problems until after the AC analysis was complete. The AC evaluated randomization balance, enrollment patterns, study visit scheduling, variability of reported data, and last digit reference. The AC classified sites as 'High Concern', 'Moderate Concern', 'Mild Concern', or 'No Concern'.
The AC correctly identified the two NDAs with data deemed unreliable by OSI. In addition, central data analysis correctly identified 5 of 6 (83%) sites for which FDA recommended rejection of data and 13 of 15 sites (87%) for which any regulatory deviations were identified during inspection. Of the six sites for which OSI reviewed inspections and found no deviations, the central process flagged four at the lowest level of concern, one at a moderate level, and one was not flagged.
Central data monitoring during the conduct of a trial while data checking was in progress was not evaluated.
Systematic central monitoring of clinical trial data can identify problems at the same trials and sites identified during FDA site inspections. Central data monitoring in conjunction with an overall monitoring process that adapts to identify risks as a trial progresses has the potential to reduce the frequency of site visits while increasing data integrity and decreasing trial costs compared to processes that are dependent primarily on source documentation.
现场监测和源文件验证占临床试验成本的 15%-30%。另一种选择是简化现场监测，重点关注通过中央数据监测识别的特定于试验的风险。与主要侧重于源文件验证的现场监测相比，这种基于风险的方法可以保持甚至提高临床试验数据和人类受试者保护的质量。
确定临床研究赞助商向美国食品和药物管理局（FDA）提交的数据的统计学家进行的中央审查是否可以识别出失败 FDA 现场检查的问题地点和试验。
一个独立的分析中心（AC）分析了来自四个匿名新药申请（NDA）的数据，FDA 在该申请中进行了由 FDA 科学调查办公室（OSI）监督的现场检查。OSI 的 FDA 团队成员从所有具有 Study Data Tabulation Model（SDTM）格式数据的 NDA 中选择了这四个 NDA。这四个 NDA 中的两个在 FDA 现场检查发现严重数据完整性问题后，OSI 认为数据不可靠。其他两个 NDA 的临床数据在现场检查后 OSI 认为是可靠的。在开始时，AC 只知道实验设计指定了两个有重大问题的 NDA。在 AC 分析完成之前，FDA 没有向 AC 提供有关哪些 NDA 有问题、检查了多少个站点或发现了多少个站点有问题的信息。AC 评估了随机化平衡、入组模式、研究访问计划、报告数据的可变性和最后一位数字参考。AC 将站点分类为“高关注”、“中度关注”、“轻度关注”或“无关注”。
AC 正确识别了 OSI 认为不可靠的数据的两个 NDA。此外，中央数据分析正确识别了 6 个（83%）FDA 建议拒绝数据的站点和 15 个（87%）在检查中发现任何监管偏差的站点。在 OSI 审查检查并发现没有偏差的六个站点中，中央过程标记了四个处于最低关注级别，一个处于中度级别，一个未标记。
在数据检查进行期间对试验进行期间的中央数据监测未进行评估。
系统的临床试验数据中央监测可以识别出与 FDA 现场检查期间相同的试验和站点的问题。中央数据监测与适应风险识别的整体监测过程相结合，随着试验的进展，具有降低现场检查频率、提高数据完整性和降低试验成本的潜力，与主要依赖源文件的过程相比。
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