F. Hoffmann-La Roche AG, 4070, Basel, Switzerland.
Merck & Co., Inc., Rahway, NJ, 07065, USA.
Ther Innov Regul Sci. 2024 Jul;58(4):591-599. doi: 10.1007/s43441-024-00631-8. Epub 2024 Apr 2.
Accurate and timely reporting of adverse events (AEs) in clinical trials is crucial to ensuring data integrity and patient safety. However, AE under-reporting remains a challenge, often highlighted in Good Clinical Practice (GCP) audits and inspections. Traditional detection methods, such as on-site investigator audits via manual source data verification (SDV), have limitations. Addressing this, the open-source R package {simaerep} was developed to facilitate rapid, comprehensive, and near-real-time detection of AE under-reporting at each clinical trial site. This package leverages patient-level AE and visit data for its analyses. To validate its efficacy, three member companies from the Inter coMPany quALity Analytics (IMPALA) consortium independently assessed the package. Results showed that {simaerep} consistently and effectively identified AE under-reporting across all three companies, particularly when there were significant differences in AE rates between compliant and non-compliant sites. Furthermore, {simaerep}'s detection rates surpassed heuristic methods, and it identified 50% of all detectable sites as early as 25% into the designated study duration. The open-source package can be embedded into audits to enable fast, holistic, and repeatable quality oversight of clinical trials.
准确、及时地报告临床试验中的不良事件(AEs)对于确保数据完整性和患者安全至关重要。然而,不良事件漏报仍然是一个挑战,这在良好临床规范(GCP)审核和检查中经常被强调。传统的检测方法,如通过手动源数据验证(SDV)进行现场研究者审核,存在局限性。为了解决这个问题,开发了开源 R 包 {simaerep},以促进在每个临床试验现场快速、全面和近乎实时地检测不良事件漏报。该软件包利用患者水平的不良事件和就诊数据进行分析。为了验证其有效性,IMPALA 联盟的三个成员公司独立评估了该软件包。结果表明,{simaerep}在所有三个公司中始终如一地有效识别不良事件漏报,特别是在合规和不合规站点之间不良事件发生率存在显著差异时。此外,{simaerep}的检测率超过了启发式方法,并且它在指定研究持续时间的 25%时就已经识别出了所有可检测站点的 50%。该开源软件包可以嵌入审核中,以便对临床试验进行快速、全面和可重复的质量监督。