Tools for assessing quality and susceptibility to bias in observational studies in epidemiology: a systematic review and annotated bibliography
Tools for assessing quality and susceptibility to bias in observational studies in epidemiology: a systematic review and annotated bibliography
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DOI：
10.1093/ije/dym018
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发表时间：
2007-06-01
影响因子：
7.7
通讯作者：
Higgins, Julian P. T.
中科院分区：
文献类型：
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作者：
Sanderson, Simon;Tatt, Lain D.;Higgins, Julian P. T.
在解释初步研究和进行系统评价和荟萃分析时，评估质量和偏倚易感性是必不可少的。评估临床试验质量的工具有很好的描述，但对观察性流行病学研究的类似工具的关注要少得多。方法通过检索三个电子数据库、参考文献和谷歌（(R)）网络检索来确定工具。两名审稿人使用预先引导的提取表格和严格的纳入标准提取数据。对可能与偏倚相关的领域的工具含量进行了评估，并根据STROBE报告观察性流行病学研究指南进行了评估。结果共审查了86个工具，包括41个简易清单、12个附加简易判断清单和33个量表。题目数从3到36（平均13.7）不等。三分之一的工具被设计为在特定的审查中单一使用，三分之一用于关键评估。一半的工具提供了开发细节，尽管大多数都是为将来在其他上下文中使用而提出的。大多数工具包括选择方法（92%）、研究变量测量（86%）、设计特定偏差来源（86%）、混淆控制（78%）和统计使用（78%）；只有4%的人提到了利益冲突。跨工具的域的分布和权重是可变和不一致的。本报告确定了一些有用的评估工具。工具应严格开发，以证据为基础，有效，可靠和易于使用。有必要就评估观察流行病学偏倚易感性的关键因素达成一致，并开发适当的评估工具。
Background Assessing quality and susceptibility to bias is essential when interpreting primary research and conducting systematic reviews and meta-analyses. Tools for assessing quality in clinical trials are well-described but much less attention has been given to similar tools for observational epidemiological studies.Methods Tools were identified from a search of three electronic databases, bibliographies and an Internet search using Google((R)). Two reviewers extracted data using a pre-piloted extraction form and strict inclusion criteria. Tool content was evaluated for domains potentially related to bias and was informed by the STROBE guidelines for reporting observational epidemiological studies.Results A total of 86 tools were reviewed, comprising 41 simple checklists, 12 checklists with additional summary judgements and 33 scales. The number of items ranged from 3 to 36 (mean 13.7). One-third of tools were designed for single use in a specific review and one-third for critical appraisal. Half of the tools provided development details, although most were proposed for future use in other contexts. Most tools included items for selection methods (92%), measurement of study variables (86%), design-specific sources of bias (86%), control of confounding (78%) and use of statistics (78%); only 4% addressed conflict of interest. The distribution and weighting of domains across tools was variable and inconsistent.Conclusion A number of useful assessment tools have been identified by this report. Tools should be rigorously developed, evidence-based, valid, reliable and easy to use. There is a need to agree on critical elements for assessing susceptibility to bias in observational epidemiology and to develop appropriate evaluation tools.