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IJE Advance Access originally published online on July 17, 2006
International Journal of Epidemiology 2006 35(4):1081-1082; doi:10.1093/ije/dyl139
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Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2006; all rights reserved.

Commentary

Commentary: Dealing with measurement error: multiple imputation or regression calibration?

Ian R White

MRC Biostatistics Unit, Institute of Public Health, Cambridge CB2 2SR, UK

E-mail: ian.white@mrc-bsu.cam.ac.uk

The first 10% of the full text of this article appears below.

Multiple imputation (MI) is a well-established method of handling missing data and is increasingly implemented in statistical software packages. Unlike other imputation methods, MI produces not one but several imputed datasets. This enables it to appropriately reflect the uncertainty due to missing data and, hence, to produce valid statistical inferences.1

Cole et al. in . . . [Full Text of this Article]


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