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Statistical Methods in Medical Research 2008, doi:10.1177/0962280207081318
Robust techniques for measurement error correction: a review
Department of Statistics, University of Padova, Italy
* To whom correspondence should be addressed.
Measurement error affecting the independent variables in regression models is a commonproblem in many scientific areas. It is well known that the implications of ignoring measurement errors in inferential procedures may be substantial, often turning out inunreliable results. Many different measurement error correction techniques have beensuggested in literature since the 80's. Most of them require many assumptions on theinvolved variables to be satisfied. However, it may be usually very hard to check whetherthese 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 formeasurement 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 modelingassumptions 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.
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