Development of the individual participant data integrity tool for assessing the integrity of randomised trials using individual participant data.

IF 5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Research Synthesis Methods Pub Date : 2024-11-01 Epub Date: 2024-08-18 DOI:10.1002/jrsm.1739
Kylie E Hunter, Mason Aberoumand, Sol Libesman, James X Sotiropoulos, Jonathan G Williams, Wentao Li, Jannik Aagerup, Ben W Mol, Rui Wang, Angie Barba, Nipun Shrestha, Angela C Webster, Anna Lene Seidler
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Abstract

Increasing integrity concerns in medical research have prompted the development of tools to detect untrustworthy studies. Existing tools primarily assess published aggregate data (AD), though scrutiny of individual participant data (IPD) is often required to detect trustworthiness issues. Thus, we developed the IPD Integrity Tool for detecting integrity issues in randomised trials with IPD available. This manuscript describes the development of this tool. We conducted a literature review to collate and map existing integrity items. These were discussed with an expert advisory group; agreed items were included in a standardised tool and automated where possible. We piloted this tool in two IPD meta-analyses (including 116 trials) and conducted preliminary validation checks on 13 datasets with and without known integrity issues. We identified 120 integrity items: 54 could be conducted using AD, 48 required IPD, and 18 were possible with AD, but more comprehensive with IPD. An initial reduced tool was developed through consensus involving 13 advisors, featuring 11 AD items across four domains, and 12 IPD items across eight domains. The tool was iteratively refined throughout piloting and validation. All studies with known integrity issues were accurately identified during validation. The final tool includes seven AD domains with 13 items and eight IPD domains with 18 items. The quality of evidence informing healthcare relies on trustworthy data. We describe the development of a tool to enable researchers, editors, and others to detect integrity issues using IPD. Detailed instructions for its application are published as a complementary manuscript in this issue.

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开发个人参与者数据完整性工具,用于利用个人参与者数据评估随机试验的完整性。
医学研究中对诚信的关注与日俱增,这促使人们开发各种工具来检测不可信的研究。现有的工具主要评估已发表的总体数据(AD),但要检测可信度问题,往往需要对个体参与者数据(IPD)进行仔细检查。因此,我们开发了IPD完整性工具,用于检测有IPD的随机试验中的完整性问题。本手稿介绍了这一工具的开发过程。我们进行了文献综述,整理并绘制了现有的诚信项目。我们与专家顾问小组讨论了这些项目;达成一致的项目被纳入标准化工具,并在可能的情况下实现了自动化。我们在两项 IPD 元分析(包括 116 项试验)中试用了这一工具,并对存在和不存在已知完整性问题的 13 个数据集进行了初步验证检查。我们确定了 120 个完整性项目:其中 54 项可以使用 AD 分析,48 项需要 IPD 分析,18 项可以使用 AD 分析,但 IPD 分析更为全面。在 13 位顾问的共同努力下,我们开发出了一个初步缩减工具,其中 11 个 AD 项目横跨 4 个领域,12 个 IPD 项目横跨 8 个领域。该工具在试用和验证过程中不断改进。在验证过程中,准确识别了所有存在已知完整性问题的研究。最终工具包括 7 个 AD 领域 13 个项目和 8 个 IPD 领域 18 个项目。为医疗保健提供依据的证据质量依赖于可信的数据。我们介绍了该工具的开发过程,该工具可帮助研究人员、编辑及其他人员利用 IPD 检测诚信问题。详细的应用说明将作为本期的补充手稿发表。
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来源期刊
Research Synthesis Methods
Research Synthesis Methods MATHEMATICAL & COMPUTATIONAL BIOLOGYMULTID-MULTIDISCIPLINARY SCIENCES
CiteScore
16.90
自引率
3.10%
发文量
75
期刊介绍: Research Synthesis Methods is a reputable, peer-reviewed journal that focuses on the development and dissemination of methods for conducting systematic research synthesis. Our aim is to advance the knowledge and application of research synthesis methods across various disciplines. Our journal provides a platform for the exchange of ideas and knowledge related to designing, conducting, analyzing, interpreting, reporting, and applying research synthesis. While research synthesis is commonly practiced in the health and social sciences, our journal also welcomes contributions from other fields to enrich the methodologies employed in research synthesis across scientific disciplines. By bridging different disciplines, we aim to foster collaboration and cross-fertilization of ideas, ultimately enhancing the quality and effectiveness of research synthesis methods. Whether you are a researcher, practitioner, or stakeholder involved in research synthesis, our journal strives to offer valuable insights and practical guidance for your work.
期刊最新文献
Automation tools to support undertaking scoping reviews. Reduce, reuse, recycle: Introducing MetaPipeX, a framework for analyses of multi-lab data. A comparison of two models for detecting inconsistency in network meta-analysis. Calculating the power of a planned individual participant data meta-analysis to examine prognostic factor effects for a binary outcome. Considerations for conducting systematic reviews: A follow-up study to evaluate the performance of various automated methods for reference de-duplication.
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