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Commentary: Calculations of EPIC proportions
*Department of Epidemiology and Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA. E-mail: stdls@channing.harvard.edu
Accepted 5 February 2008
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Rosner et al.1 developed the necessary methods for the correction of bias due to measurement error in multivariate models in 1990. Although originally focussed on relative risk estimation and inference from logistic regression models, their method was extended in a completely straightforward manner to the Cox model for survival data analysis in a rare disease setting,2,3 as occurs in prospective studies of cancer incidence including the European Prospective Investigation into Cancer and Nutrition (EPIC) study,4 in the Pooling Project of Prospective Studies of Diet and Cancer in Men and Women5 and elsewhere. Smith-Warner and colleagues have successfully applied this method to numerous investigations from the Pooling Project of Prospective Studies of Diet and Cancer in Men and Women.6–11 In this commentary, we will explore how the methods presented in Ferrari et al.'s12 paper differ from Rosner, Spiegelman and Willett's,1,2 and, where differences are apparent, what are the advantages and