Regression calibration for Cox regression under heteroscedastic measurement error - Determining risk factors of cardiovascular diseases from error-prone nutritional replication data

For instance nutritional data are often subject to severe
measurement error, and an adequate adjustment of the estimators is
indispensable to avoid deceptive conclusions. This paper discusses
and extends the method of regression calibration to correct for
measurement error in Cox regression. Special attention is paid to
the modelling of quadratic predictors, the role of heteroscedastic
measurement error, and the efficient use of replicated measurements
of the surrogates. The method is used to analyze data from the
German part of the MONICA cohort study on cardiovascular diseases.
The results corroborate the importance of taking into account
measurement error carefully.