Boccia Stefania, La Torre Giuseppe, Persiani Roberto, D'Ugo Domenico, van Duijn Cornelia M, Ricciardi Gualtiero
Institute of Hygiene, Catholic University of the Sacred Heart, Rome, Italy.
World J Emerg Surg. 2007 Mar 15;2:7. doi: 10.1186/1749-7922-2-7.
Scientific literature may be biased because of the internal validity of studies being compromised by different forms of measurement error, and/or because of the selective reporting of positive and 'statistically significant' results. While the first source of bias might be prevented, and in some cases corrected to a degree, the second represents a pervasive problem afflicting the medical literature; a situation that can only be 'corrected' by a change in the mindset of authors, reviewers, and editors. This review focuses on the concepts of confounding, selection bias and information bias, utilising explanatory examples and simple rules to recognise and, when possible, to correct for them. Confounding is a mixing of effects resulting from an imbalance of some of the causes of disease across the compared groups. It can be prevented by randomization and restriction, and controlled by stratification, standardization or by using multivariable techniques. Selection bias stems from an absence of comparability among the groups being studied, while information bias arises from distorted information collection techniques. Publication bias of medical research results can invalidate evidence-based medicine, when a researcher attempting to collect all the published studies on a specific topic actually gathers only a proportion of them, usually the ones reporting 'positive' results. The selective publication of 'statistically significant' results represents a problem that researchers and readers have to be aware of in order to face the entire body of published medical evidence with a degree of scepticism.
科学文献可能存在偏差，这是因为研究的内部有效性受到不同形式测量误差的影响，和/或因为对阳性和“统计学显著”结果的选择性报告。虽然第一种偏差来源或许可以预防，在某些情况下还能在一定程度上得到纠正，但第二种偏差是困扰医学文献的一个普遍问题；这种情况只有通过改变作者、审稿人和编辑的思维方式才能“纠正”。本综述聚焦于混杂、选择偏倚和信息偏倚的概念，运用解释性示例和简单规则来识别它们，并在可能的情况下对其进行纠正。混杂是指由于疾病的某些病因在比较组之间分布不均衡而导致的效应混合。它可以通过随机化和限制来预防，并通过分层、标准化或使用多变量技术来控制。选择偏倚源于所研究组之间缺乏可比性，而信息偏倚则源于信息收集技术的扭曲。当研究人员试图收集关于某个特定主题的所有已发表研究时，实际上只收集到了其中一部分，通常是那些报告“阳性”结果的研究，此时医学研究结果的发表偏倚会使循证医学失效。“统计学显著”结果的选择性发表是一个研究人员和读者都必须意识到的问题，以便带着一定程度的怀疑态度面对已发表的全部医学证据。
