Guolo Annamaria
Department of Statistics, University of Padova, Italy.
Stat Methods Med Res. 2008 Dec;17(6):555-80. doi: 10.1177/0962280207081318. Epub 2008 Mar 28.
Measurement error affecting the independent variables in regression models is a common problem in many scientific areas. It is well known that the implications of ignoring measurement errors in inferential procedures may be substantial, often turning out in unreliable results. Many different measurement error correction techniques have been suggested in literature since the 80's. Most of them require many assumptions on the involved variables to be satisfied. However, it may be usually very hard to check whether these assumptions are satisfied, mainly because of the lack of information about the unobservable and mismeasured phenomenon. Thus, alternatives based on weaker assumptions on the variables may be preferable, in that they offer a gain in robustness of results. In this paper, we provide a review of robust techniques to correct for measurement errors affecting the covariates. Attention is paid to methods which share properties of robustness against misspecifications of relationships between variables. Techniques are grouped according to the kind of the underlying modeling assumptions and the inferential methods. Details about the techniques are given and their applicability is discussed. The basic framework is the epidemiological setting, where literature about the measurement error phenomenon is very substantial.
影响回归模型中自变量的测量误差是许多科学领域中常见的问题。众所周知，在推理过程中忽略测量误差的影响可能很大，往往会导致不可靠的结果。自20世纪80年代以来，文献中提出了许多不同的测量误差校正技术。其中大多数需要满足关于所涉及变量的许多假设。然而，通常很难检查这些假设是否得到满足，主要是因为缺乏关于不可观测和测量错误现象的信息。因此，基于对变量较弱假设的替代方法可能更可取，因为它们能提高结果的稳健性。在本文中，我们对校正影响协变量的测量误差的稳健技术进行了综述。重点关注那些对变量之间关系的错误设定具有稳健性的方法。技术根据基础建模假设的类型和推理方法进行分组。给出了技术的详细信息并讨论了它们的适用性。基本框架是流行病学背景，关于测量误差现象的文献非常丰富。
