Regression calibration when foods (measured with error) are the variables of interest: markedly non-Gaussian data with many zeroes.

ABSTRACT: Regression calibration has been described as a means of correcting effects of measurement error for normally distributed dietary variables. When foods are the items of interest, true distributions of intake are often positively skewed, may contain many zeroes, and are usually not described by well-known statistical distributions. The authors considered the validity of regression calibration assumptions where data are non-Gaussian. Such data (including many zeroes) were simulated, and use of the regression calibration algorithm was evaluated. An example used data from Adventist Health Study 2 (2002-2008). In this special situation, a linear calibration model does (as usual) at least approximately correct the parameter that captures the exposure-disease association in the "disease" model. Po

SUBMITTER: Fraser GE 

PROVIDER: S-EPMC3271814 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature