Björn Koneswarakantha, Ronojit Adyanthaya, Jennifer Emerson, Frederik Collin, Annett Keller, Michaela Mattheus, Ioannis Spyroglou, Sandra Donevska, Timothé Ménard
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引用次数: 0
摘要
准确及时地报告临床试验中的不良事件 (AE) 对于确保数据完整性和患者安全至关重要。然而,AE 报告不足仍是一项挑战,经常在良好临床实践(GCP)审核和检查中被强调。传统的检测方法存在局限性,例如通过人工源数据验证(SDV)对研究者进行现场审核。为了解决这个问题,我们开发了开源 R 软件包 {simaerep},以方便快速、全面、近乎实时地检测每个临床试验机构的 AE 少报情况。该软件包利用患者水平的 AE 和访视数据进行分析。为了验证它的有效性,Inter coMPany quALity Analytics (IMPALA) 联盟的三家成员公司对该软件包进行了独立评估。结果显示,{simaerep}能持续有效地识别三家公司的AE漏报情况,尤其是当合规和不合规医疗机构之间的AE发生率存在显著差异时。此外,{simaerep}的检测率也超过了启发式方法,它能在指定研究持续时间的25%时就识别出50%的检测点。该开源软件包可嵌入审计中,实现快速、全面、可重复的临床试验质量监督。
An Open-Source R Package for Detection of Adverse Events Under-Reporting in Clinical Trials: Implementation and Validation by the IMPALA (Inter coMPany quALity Analytics) Consortium.
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.