Tools for assessing quality and susceptibility to bias in observational studies in epidemiology: a systematic review and annotated bibliography

观察研究 加强流行病学观察研究报告 数据提取 批判性评价 混淆 选择偏差 临床研究设计 流行病学 系统回顾 计算机科学 梅德林 出版偏见 质量(理念) 信息偏差 加权 数据科学 统计 医学 荟萃分析 临床试验 数学 替代医学 病理 政治学 法学 放射科 哲学 认识论
作者
Saskia C. Sanderson,ID Tatt,Julian P. T. Higgins
出处
期刊:International Journal of Epidemiology [Oxford University Press]
卷期号:36 (3): 666-676 被引量:1480
标识
DOI:10.1093/ije/dym018
摘要

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®. 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.
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