Commentary: Dealing with measurement error: multiple imputation or regression calibration?
2006; Oxford University Press; Volume: 35; Issue: 4 Linguagem: Inglês
10.1093/ije/dyl139
ISSN1464-3685
Autores Tópico(s)Statistical Methods and Inference
ResumoCole et al. in this issue 2 propose that MI may also be useful in dealing with a second problem rife in epidemiology: exposure measurement error, which typically causes underestimation of exposure–disease associations (regression dilution bias). 3 They coin the acronym MIME (multiple imputation for measurement error) and show that this method can indeed remove regression dilution bias. How widely should MIME be used? Unfortunately, MIME is only appropriate for measurement error problems in which the true exposure is measured in a sub-sample (a validation study). This is because MIME involves fitting a regression model of true exposures on observed exposures, in order to impute the unobserved true exposures. Often, the degree of measurement error is assessed by taking repeat measurements (a repeatability study). 4 In such cases, the true exposure is never observed, so MIME as described by Cole et al. would not be appropriate (and complex modifications would be required to make MIME work). The main alternative to MIME is regression calibration (RC). 5
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