A unified framework for bias assessment in clinical research

保护 统一 多样性(控制论) 数据科学 计算机科学 质量(理念) 管理科学 风险分析(工程) 外部有效性 知识管理 心理学 工程类 医学 人工智能 社会心理学 认识论 哲学 护理部 程序设计语言
作者
Jennifer Stone,Kathryn Glass,Justin Clark,Zachary Munn,Peter Tugwell,Suhail A.R. Doi
出处
期刊:International Journal of Evidence-based Healthcare [Lippincott Williams & Wilkins]
卷期号:17 (2): 106-120 被引量:36
标识
DOI:10.1097/xeb.0000000000000165
摘要

Methodological flaws, limitations, and inadequate practices in research are well known and pose threats to the internal validity of any research study. However, there are ways of safeguarding research conduct to reduce the chance of research producing distorted results. Numerous tools now exist to assess the incorporation of such safeguards into primary research studies (also known as quality and/or risk-of-bias assessment). These tools typically include a variety of items that are then checked against those implemented in the study. Despite a lot of research in this area, no comprehensive generic classification of safeguards across study designs exist, although attempts have been made to clarify aspects of this. We review the developments in this area as well as use preliminary data from 100 methodological studies to illustrate our proposed approach. We conclude by proposing a new framework for identifying research studies at risk of being biased and the information in this article will promote a unification of the diverse approaches to facilitating bias assessment in clinical research.
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