The GRACE Checklist: A Validated Assessment Tool for High Quality Observational Studies of Comparative Effectiveness
The GRACE Checklist: A Validated Assessment Tool for High Quality Observational Studies of Comparative Effectiveness
复制标题
DOI：
10.18553/jmcp.2016.22.10.1107
复制
发表时间：
2016-10-01
影响因子：
2.1
通讯作者：
Velentgas, Priscilla
中科院分区：
文献类型：
--
作者：
Dreyer, Nancy A.;Bryant, Allison;Velentgas, Priscilla
背景技术背景：认识到对现实世界人群中治疗有效性的强有力证据的需求日益增长，已经为比较有效性的非干预性研究制定了良好研究（GRACE）指南，以确定哪些研究足够严格，足以可靠地用于卫生技术评估。目的：评估GRACE检查表的哪些方面对质量认可贡献最大。方法：我们收集了2001年至2010年发表的28篇观察性比较有效性文章，比较了药物、医疗器械、和医疗程序。来自学术界、制药公司和政府机构的22名志愿者对这些文章进行了GRACE检查表，提供了56项评估。10位资深学术和行业专家对文章的总体质量进行了评估，以支持决策。我们还根据文章发表的期刊的年度引用次数和影响因子对每篇文章进行了评级。为了确定最能预测质量的检查表项目，分类和回归树（CART）分析，使用二元递归分区方法创建3个决策树，将56篇文章评估与3个外部质量结果进行比较：（1）总体质量专家评估，（2）引用频率和（3）影响因子。第四棵树查看所有3个质量指标的综合结果。结果：质量的最佳预测因素包括以下内容：使用同期对照药物，将研究限制在研究药物的新启动者，研究组结局的等效测量，收集大多数（如果不是所有）已知混杂因素或效应调节因素的数据，解释分析中的永久时间偏倚，并使用敏感性分析来测试影响估计值在多大程度上取决于各种假设。只有敏感性分析出现一致的预测质量在所有4棵树。当使用3个质量指标的复合结果时，GRACE检查表显示出高灵敏度和特异性（分别为71.43%和80.95%）。结论：GRACE检查表因其广泛的验证工作而从其他共识驱动和专家指导文件中脱颖而出。最近的工作表明，该清单具有很强的敏感性和特异性，增加了其作为筛选工具的实用性，以确定值得深入审查的高质量观察性比较有效性研究和决策支持的适用性。版权所有（C）2016，管理护理药房学院. All rights reserved.
BACKGROUND: Recognizing the growing need for robust evidence about treatment effectiveness in real-world populations, the Good Research for Comparative Effectiveness (GRACE) guidelines have been developed for noninterventional studies of comparative effectiveness to determine which studies are sufficiently rigorous to be reliable enough for use in health technology assessments.OBJECTIVE: To evaluate which aspects of the GRACE Checklist contribute most strongly to recognition of quality.METHODS: We assembled 28 observational comparative effectiveness articles published from 2001 to 2010 that compared treatment effectiveness and/or safety of drugs, medical devices, and medical procedures. Twenty-two volunteers from academia, pharmaceutical companies, and government agencies applied the GRACE Checklist to those articles, providing 56 assessments. Ten senior academic and industry experts provided assessments of overall article quality for the purpose of decision support. We also rated each article based on the number of annual citations and impact factor of the journal in which the article was published. To identify checklist items that were most predictive of quality, classification and regression tree (CART) analysis, a binary, recursive, partitioning methodology, was used to create 3 decision trees, which compared the 56 article assessments with 3 external quality outcomes: (1) expert assessment of overall quality, (2) citation frequency, and (3) impact factor. A fourth tree looked at the composite outcome of all 3 quality indicators.RESULTS: The best predictors of quality included the following: use of concurrent comparators, limiting the study to new initiators of the study drug, equivalent measurement of outcomes in study groups, collecting data on most if not all known confounders or effect modifiers, accounting for immortal time bias in the analysis, and use of sensitivity analyses to test how much effect estimates depended on various assumptions. Only sensitivity analyses appeared consistently as a predictor of quality in all 4 trees. When a composite outcome of the 3 quality measures was used, the GRACE Checklist showed high sensitivity and specificity (71.43% and 80.95%, respectively).CONCLUSIONS: The GRACE Checklist stands out from other consensus driven and expert guidance documents because of its extensive validation efforts. This most recent work shows that the checklist has strong sensitivity and specificity, increasing its utility as a screening tool to identify high-quality observational comparative effectiveness research worthy of in-depth review and applicability for decision support. Copyright (C) 2016, Academy of Managed Care Pharmacy. All rights reserved.