Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PEI C1A 4P3, Canada.
Prev Vet Med. 2014 Feb 15;113(3):331-7. doi: 10.1016/j.prevetmed.2013.10.008. Epub 2013 Oct 16.
Observational studies are prone to two types of errors: random and systematic. Random error arises as a result of variation between samples that might be drawn in a study and can be reduced by increasing the sample size. Systematic error arises from problems with the study design or the methods used to obtain the study data and is not influenced by sample size. Over the last 20 years, veterinary epidemiologists have made great progress in dealing more effectively with random error (particularly through the use of multilevel models) but paid relatively little attention to systematic error. Systematic errors can arise from unmeasured confounders, selection bias and information bias. Unmeasured confounders include both factors which are known to be confounders but which were not measured in a study and factors which are not known to be confounders. Confounders can bias results toward or away from the null. The impact of selection bias can also be difficult to predict and can be negligible or large. Although the direction of information bias is generally toward the null, this cannot be guaranteed and its impact might be very large. Methods of dealing with systematic errors include: qualitative assessment, quantitative bias analysis and incorporation of bias parameters into the statistical analyses.
随机错误和系统错误。随机错误是由于研究中可能抽取的样本之间的差异而产生的，可以通过增加样本量来减少。系统错误是由于研究设计或获取研究数据的方法存在问题而产生的，不受样本量的影响。在过去的 20 年中，兽医流行病学家长期致力于更有效地处理随机错误（特别是通过使用多层次模型），但相对较少关注系统错误。系统错误可能源于未测量的混杂因素、选择偏差和信息偏倚。未测量的混杂因素包括已知是混杂因素但在研究中未测量的因素，以及未知是混杂因素的因素。混杂因素可能使结果偏向或远离零假设。选择偏差的影响也难以预测，可能微不足道或很大。虽然信息偏倚的方向通常是零假设，但这不能保证，其影响可能非常大。处理系统错误的方法包括：定性评估、定量偏差分析以及将偏差参数纳入统计分析。
