Coupling Results Data from ClinicalTrials.gov and Bibliographic Databases to Accelerate Evidence Synthesis

耦合来自 ClinicalTrials.gov 和书目数据库的结果数据以加速证据合成

• 批准号: 10357922
• 负责人: FLORENCE BOURGEOIS
• 金额: $ 32.8万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10357922
• 关键词: Adverse drug event; Adverse event; Bibliographic Databases; Bibliography; Clinical; Clinical Medicine; Clinical Research; Clinical Trials; Consumption; Coupling; Data; Data Reporting; Data Set; Data Sources; Databases; Detection; Devices; Drug Monitoring; Drug usage; Equilibrium; Evidence Based Medicine; Exposure to; Foundations; Goals; Health; Heterogeneity; Individual; Informatics; Information Retrieval; Infrastructure; International; Intervention; Learning; Link; Machine Learning; Manuals; Maps; Meta-Analysis; Methods; Monitor; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Oncology; Outcome; Participant; Patients; Performance; Pharmaceutical Preparations; Policies; Procedures; Process; Public Health Informatics; Publishing; Records; Registries; Reporting; Research Personnel; Resources; Risk; Rofecoxib; Safety; Signal Transduction; Site; Standardization; Structure; Time; Unified Medical Language System; Validation; Work; adverse event monitoring; base; cluster trial; concept mapping; design; drug testing; innovation; machine learning method; medication safety; novel; rosiglitazone; safety outcomes; screening; supervised learning; systematic review; tool; treatment arm; trial design; validation studies; vector

Project Summary Clinical trials are foundational to evidence-based medicine, but results reporting from trials is incomplete and frequently delayed. It is estimated that as many as half of clinical trials are not published and as many as half of published trials underreport or misreport outcomes. This type of results reporting distorts the evidence available to clinicians—particularly when it comes to assessing the safety of interventions like drugs and devices—and may place patients at unnecessary risk. There is a critical need for novel methods to identify and monitor drug safety data. Through the infrastructure provided by ClinicalTrials.gov, structured trial results (including safety findings) are now becoming available for an increasing number of trials in a comprehensive and timely fashion. However, access and use of these data in evidence synthesis tasks remain limited. ClinicalTrials.gov is the largest single registry for clinical studies worldwide and includes more than 260,000 registered studies. Of the 108,941 completed trials registered with the site, 20% have uploaded results data for a total of 7.85 million participants. Results data reported on ClinicalTrials.gov have the potential to fill gaps created by delays and biases in published articles and provide an earlier and more complete overview of available trial evidence. We propose to develop novel informatics approaches based on combinations of information retrieval and machine learning methods to facilitate access and analysis of trial results reported in this registry. Focusing on trials testing drug interventions in type 2 diabetes, obesity, and oncology, we perform this work in three specific aims: 1) Develop semi-automated trial screening for identifying and aggregating trials relevant to a clinical intervention; 2) Extract adverse event and safety outcomes data from results reported in the registry; and 3) Perform validation studies to assess detection of adverse events and performance of semi- automated meta-analyses of safety outcomes. Methods developed in this project will facilitate timely, broad- scale use of trial results reported on ClinicalTrials.gov in order to augment the availability of comprehensive and timely drug safety data. All methods will be made publicly available in order to support adverse event monitoring and systematic reviews of drug interventions.

项目摘要 临床试验是循证医学的基础，但试验结果的报告是不完整的， 经常延误。据估计，多达一半的临床试验没有发表，多达一半 少报或误报结果的已发表试验。这种类型的结果报告扭曲了证据 可供临床医生使用-尤其是在评估药物和药物等干预措施的安全性时 设备-并可能将患者置于不必要的风险中。迫切需要新的方法来识别和识别 监测药品安全数据。通过ClinicalTrials.gov提供的基础设施，结构化试验结果 (包括安全发现)现在可以用于越来越多的综合试验 和及时的时尚。然而，在证据合成任务中获取和使用这些数据仍然有限。 ClinicalTrials.gov是全球最大的临床研究单一注册中心，包括超过260,000 注册研究。在该网站注册的108,941项已完成试验中，20%的人上传了结果数据 共有785万人参加。结果在ClinicalTrials.gov上报告的数据有可能填补空白 由已发表文章中的延迟和偏见创建，并提供了更早和更完整的概述 现有的审判证据。我们建议开发基于组合的新的信息学方法 信息检索和机器学习方法，以方便访问和分析#年报告的试验结果 这个注册表。专注于测试药物干预在2型糖尿病、肥胖症和肿瘤学中的试验，我们执行 这项工作有三个具体目标：1)开发用于识别和汇总试验的半自动试验筛选 与临床干预相关；2)从报告的结果中提取不良事件和安全结果数据 注册处；以及3)进行验证研究，以评估不良事件检测和半 安全结果的自动荟萃分析。本项目开发的方法将促进及时、广泛的 扩大对ClinicalTrials.gov上报告的试验结果的使用，以增加综合 和及时的药品安全数据。所有方法都将公开提供，以支持不良事件 监测和系统审查药物干预措施。