Task 2: Key Concepts about Measurement Error

Two of the primary challenges of dietary intake data are measurement error adjustment, and positively skewed data.  Regression calibration (see Module 21, Task 1 for more information on this topic) is a method for dealing with the former challenge.  In this method, the 24-hour recall data are replaced with a predictor of true intake, given the 24-hour recall data.  When the data are normally distributed, this estimate is fairly easy to obtain.  However, when data are positively skewed and must be transformed, the process for obtaining the regression calibration predictor involves numerical integration.  The NCI method uses adaptive Gaussian quadrature to obtain this estimate.

When regression calibration is used, it is necessary to include the covariates that will be used in the health parameter model in the calculation of the regression calibration predictor.  In other words,

equation for Gaussian quadrature

Furthermore, the covariates X may comprise covariates that will aid in the prediction of estimating T hat.These covariates may include data from a food frequency questionnaire (FFQ). (It is assumed the food frequency information is not related to the health parameter if true dietary intake is known.)


Note: If a food frequency variable is used as a covariate, BRR weights for the FFQ sample should be used.

This predictor T hat is then used to estimate the relationship between usual dietary intake and the health parameter, using an appropriate statistical model and a transformation of T (Box-Cox). BRR

is used to calculate standard errors (See Module 18, Task 4 for more information on this topic).

Software is available to apply this method, including the MIXTRAN, DISTRIB, and INDIVINT macros, which may be downloaded from the NCI website.


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